Wiki FKKT - User contributions [en]

2015-bionano-seminar

2015-05-03T21:02:33Z

MajaRemskar: /* Seznam seminarjev */

= Bionanotehnologija- seminar =
doc. dr. Gregor Gunčar, K2.022

== Seznam seminarjev ==
{| {{table}}
| align="center" style="background:#f0f0f0;"|'''Avtor 1'''
| align="center" style="background:#f0f0f0;"|'''Avtor 2'''
| align="center" style="background:#f0f0f0;"|'''Naslov seminarja'''
| align="center" style="background:#f0f0f0;"|'''Datum za oddajo'''
| align="center" style="background:#f0f0f0;"|'''Datum predstavitve'''
| align="center" style="background:#f0f0f0;"|'''Recenzent 1'''
| align="center" style="background:#f0f0f0;"|'''Recenzent 2'''
|-
| Anže Prašnikar||Monika Praznik ||||29.03.||31.03.||Aneja Tuljak||Angelika Vižintin
|-
| Varja Božič||Eva Knapič||Razgradljivi kondomi s protimikrobno zaščito||29.03.||31.03.||Eva Udovič||Maja Grdadolnik
|-
| Belkisa Velagić||Aleksander Benčič||Avtomobilski encimski katalizator||29.03.||31.03.||Nika Kurinčič||Tjaša Goričan
|-
| Naja Vrankar||Valter Bergant||||31.03.||02.04.||Nataša Žigante||Luka Smole
|-
| Tilen Volčanšek||Veronika Jarc||||31.03.||02.04.||Anže Prašnikar||Jakob Gašper Lavrenčič
|-
| Tanja Lipec||Iza Ogris||Test občutljivosti na gluten z neprebavljivo kapsulo||31.03.||02.04.||Varja Božič||Klara Tereza Novoselc
|-
| Katja Lovrin||Mitja Crček||Uporaba nitrifikacijskih encimov pri kmetovanju||05.04.||07.04.||Belkisa Velagić||Monika Praznik
|-
| Saša Balažic||Urban Javoršek ||||05.04.||07.04.||Naja Vrankar||Eva Knapič
|-
| Urban Borštnik||Sara Primec||||05.04.||07.04.||Tilen Volčanšek||Aleksander Benčič
|-
| Nives Ahlin||Kim Kos||||12.04.||14.04.||Tanja Lipec||Valter Bergant
|-
| Matic Bevec||Estera Merljak||Antimaček®||12.04.||14.04.||Katja Lovrin||Veronika Jarc
|-
| Vida Špindler||Jernej Pušnik||||12.04.||14.04.||Saša Balažic||Iza Ogris
|-
| Jasmina Sedmak||Maxi Sagmeister||Brezžični zobni nanobiosenzor ||19.04.||21.04.||Urban Borštnik||Mitja Crček
|-
| Sanja Popović||Benjamin Bajželj||||19.04.||21.04.||Nives Ahlin||Urban Javoršek
|-
| Blaž Komar||Alja Zottel||||19.04.||21.04.||Matic Bevec||Sara Primec
|-
| Blaž Perič||Katarina Uršič||||03.05.||05.05.||Simon Preložnik||Kim Kos
|-
| Simon Preložnik||Maja Remškar||Preprost dostavni sistem za omega-3 maščobne kisline||03.05.||05.05.||Jasmina Sedmak||Estera Merljak
|-
| Aneja Tuljak||Tina Gregorič||Stekleničke z biosenzorjem za detekcijo ''E.coli''||03.05.||05.05.||Sanja Popović||Jernej Pušnik
|-
| Damir Hamulić||Anita Kustec||||10.05.||12.05.||Blaž Komar||Maxi Sagmeister
|-
| Janja Fortin||Tina Snoj||||10.05.||12.05.||Blaž Perič||Benjamin Bajželj
|-
| Rajko Vnuk||Mojca Banič||||10.05.||12.05.||Vida Špindler||Alja Zottel
|-
| Rok Grm||Ajda Rojc||||17.05.||19.05.||Kaja Javoršek||Katarina Uršič
|-
| Kristina Gavranić||Barbara Žužek||||17.05.||19.05.||Damir Hamulić||Maja Remškar
|-
| Urška Mohorič||Griša Prinčič||||17.05.||19.05.||Janja Fortin||Tina Gregorič
|-
| Maja Ramić||Nejc Petrišič||||24.05.||26.05.||Rajko Vnuk||Anita Kustec
|-
| Barbara Jeras||Tamara Marić||||24.05.||26.05.||Rok Grm||Tina Snoj
|-
| Matic Urlep||Samo Zakotnik||||24.05.||26.05.||Kristina Gavranić||Mojca Banič
|-
| Urban Verbič||Angelika Vižintin||||31.05.||02.06.||Urška Mohorič||Ajda Rojc
|-
| Nataša Žigante||Maja Grdadolnik||||31.05.||02.06.||Maja Ramić||Barbara Žužek
|-
| Kaja Javoršek||Tjaša Goričan||||31.05.||02.06.||Barbara Jeras||Griša Prinčič
|-
| Eva Udovič||Luka Smole||||07.06.||09.06.||Matic Urlep||Nejc Petrišič
|-
| Nika Kurinčič||Jakob Gašper Lavrenčič||||07.06.||09.06.||Urban Verbič||Tamara Marić
|-
| Klara Tereza Novoselc||||||07.06.||09.06.||Samo Zakotnik||
|}

== Gradivo za predavanja ==
Gradivo za predavanja najdete v [http://ucilnica.fkkt.uni-lj.si/ spletni učilnici].

==Naloga==
'''Vaša naloga je: '''
Po dva študenta skupaj pripravita projektno nalogo iz področja Bionanotehnologije. Najpomembnejša je originalna ideja za nek izvedljiv projekt.
Predlagana struktura:
* Uvod
* Predstavitev problema, znanstvena izhodišča, cilji
* Izvedba projekta, metodologija, tehnike, materiali, vprašanja, hipoteze
* Literatura

Za pripravo seminarja velja naslednje: 
* Prva stran seminarja naj vsebuje naslov projekta, avtorje, povzetek (od 130 do 160 besed) in grafični povzetek (čez približno pol strani)
* Seminar pripravite v obliki seminarske naloge na ~5 straneh A4 (pisava 12, enojni razmak, 2,5 cm robovi). Zelo pomembno je, da je obseg od 1500 do 2000 besed . Seminarska naloga mora vsebovati najmanj tri slike. Slika mora imeti legendo in v besedilu mora biti na ustreznem mestu sklic na sliko. 
* Seminar oddajte do datuma oddaje, ki je naveden v tabeli v elektronski obliki z uporabo [http://bio.ijs.si/~zajec/poslji/ tega obrazca].
* Vsi seminarji so v elektronski obliki dostopni [http://bio.ijs.si/~zajec/poslji/bioseminar/ tukaj].
* Ustna predstavitev sledi na dan, ki je vpisan v tabeli. Za predstavitev je na voljo 20 minut, predstavitev pa ne sme biti krajša od 15 minut (popust :-)). Nalogo predstavita oba študenta (razdelita si čas). Recenzenti morajo biti na predstavitvi prisotni.
* Predstavitvi sledi razprava. Recenzenti podajo pripombe k projektu in postavijo po dve vprašanji.
* Na dan predstavitve morate docentu še pred predstavitvijo oddati končno verzijo seminarja v enem izvodu, elektronsko verzijo seminarja in predstavitev pa oddati na strežnik na dan predstavitve do polnoči.

==Imena datotek==
Prosim vas, da vse datoteke poimenujete po naslednjem receptu:
* 19_nano_Priimek1_Priimek2.doc(x) za seminar, npr. 19_nano_Craik_Venter.docx
* 19_nano_Priimek1_Priimek2.ppt(x) za prezentacijo, npr. 19_nano_Craik_Venter.pptx

==Ocenjevanje seminarjev==
Recenzenti ocenijo seminar tako, da izpolnijo [https://docs.google.com/forms/d/1WdCXoXo1zkRrVlLKIcEV1z_MyhavU-3ERBm9n2oiawI/viewform recenzentsko poročilo] na spletu. Recenzentsko poročilo morate oddati najkasneje do predstavitve seminarja.

== Mnenje o predstavitvi ==
Vsak posameznik '''mora''' oceniti seminar, tako da odda svoje [https://docs.google.com/forms/d/1ToLPn78T9W3G6Hm5hV0mLseFYghiLQMlRPGb0J5zft8/viewform mnenje] najkasneje v sedmih dneh po predstavitvi. Kdor na seminarju ni bil prisoten, mnenja '''ne sme''' oddati.

==Urejanje spletnih strani na wikiju==
Wiki so razvili zato, da lahko spletne vsebine ureja vsakdo. Ukazi so preprosti, dokler si ne zamislite česa prav posebnega. Vseeno pa je Word v primerjavi z wikijem pravo čudežno orodje... Če imate težave z oblikovanjem besedila, si preberite poglavje o urejanju wiki-strani na Wikipediji ([http://en.wikipedia.org/wiki/Help:Editing tule] v angleščini in [http://sl.wikipedia.org/wiki/Wikipedija:Urejanje_strani tu] v slovenščini). Pomaga tudi, če pogledate, kako je zapisana kakšna stran, ki se vam zdi v redu: kliknite na zavihek 'Uredite stran' in si poglejte, kako so vpisane povezave, kako nov odstavek in podobno. ''Na koncu seveda pod oknom za urejanje kliknite na 'Prekliči'.''

==Citiranje virov==
Citiranje je možno po več shemah, važno je, da se držite ene same. V seminarskih nalogah in diplomskih nalogah FKKT uprabljajte shemo citiranja, ki je pobarvana zeleno.
Temeljno načelo je, da je treba vir navesti na tak način, da ga je mogoče nedvoumno poiskati.
Za citate v naravoslovju je najpogostejše citiranje po pravilniku ISO 690. [http://www.zveza-zotks.si/gzm/dokumenti/literatura.html Pravila], ki upoštevajo omenjeni standard, so pripravili pri ZTKS. Sicer pa ima vsaka revija lahko svoj način citiranja, ki ga je treba pri pisanju članka upoštevati. 
'''Citiranje knjig:''' 
Priimek, I. ''Naslov''. Kraj: Založba, letnica. 
Priimek, I. ''Naslov: podnaslov''. Izdaja. Kraj: Založba, letnica. Zbirka, številka. ISBN. 

Boyer, R. ''Temelji biokemije''. Ljubljana: Študentska založba, 2005. 
Glick BR in Pasternak JJ. ''Molecular biotechnology: principles and applications of recombinant DNA''. 3. izdaja. Washington: ASM Press, 2003. ISBN 1-55581-269-4. 
Če so avtorji trije, je beseda in med drugim in tretjim avtorjem. Če so avtorji več kot trije, napišemo samo prvega in dopišemo ''et al''. (in drugi, po latinsko). Vse, kar je latinsko, pišemo poševno (npr. tudi imena rastlin in živali, pojme ''in vivo'', ''in vitro'' ipd.).

'''Citiranje člankov:''' 
Priimek, I. Naslov. ''Naslov revije'', letnica, letnik, številka, strani. 
Lartigue, C., Glass, J. I., Alperovich, N., Pieper, R., Parmar, P. P., Hutchison III, C. A., Smith, H. O. in Venter, J. C.
Genome transplantation in bacteria: changing one species to another. ''Science'', 2007, 317, str. 632-638.

Alternativni način citiranja (predvsem v družboslovju) je po pravilih APA, kjer članke citirajo takole: 
Priimek, I. (letnica, številka). Naslov. Naslov revije, strani. 
Lartigue C. ''et al.'' (2007, 317) Genome transplantation in bacteria: changing one species to another. ''Science'', 632-638.

Revija Science uporablja skrajšani zapis: 
C. Lartigue ''et al''. Science 317, 632 (2007) 

V diplomah na FKKT je treba navesti vire tako, da izpišete tudi naslov citiranega dela in strani od-do (ne samo začetne). Navesti morate tudi vse avtorje dela, razen v primeru, ko jih je 10 ali več. Takrat navedite le prvih devet, za ostale pa uporabite okrajšavo in sod. (in sodelavci). Pred zadnjim avtorjem naj bo vedno besedica "in".

'''Citiranje spletnih virov:''' 
Priimek, I. ''Naslov dokumenta''. Izdaja. Kraj: Založnik, letnica. Datum zadnjega popravljanja. [Datum citiranja.] spletni naslov 
strangeguitars. ''On the brink of artificial life''. 6. 10. 2007. [citirano 13. 11. 2007] http://www.metafilter.com/65331/On-the-brink-of-artificial-life 
Navedemo čim več podatkov; pogosto vseh iz pravila ne boste našli.

Combinatorial synthesis of genetic networks

2015-01-25T17:20:56Z

MajaRemskar:

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002.

(Maja Remškar)

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

=== Transcriptional regulators ===
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylaclac is compatible with xcI, ycI is compatible with xtet, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].

There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-25T17:19:40Z

MajaRemskar:

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

=== Transcriptional regulators ===
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylaclac is compatible with xcI, ycI is compatible with xtet, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].

There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T18:35:35Z

MajaRemskar: /* Conclusion */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

=== Transcriptional regulators ===
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylaclac is compatible with xcI, ycI is compatible with xtet, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].

There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:42:26Z

MajaRemskar: /* Transcriptional regulators */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

=== Transcriptional regulators ===
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylaclac is compatible with xcI, ycI is compatible with xtet, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:41:28Z

MajaRemskar: /* References */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylaclac is compatible with xcI, ycI is compatible with xtet, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:40:37Z

MajaRemskar: /* Methods */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylaclac is compatible with xcI, ycI is compatible with xtet, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:40:19Z

MajaRemskar: /* Methods */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylaclac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:38:30Z

MajaRemskar: /* Methods */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ- and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:34:35Z

MajaRemskar: /* Discussion */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

=== Conclusion ===
The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:31:04Z

MajaRemskar: /* References */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

[[SB students resources]]

Combinatorial synthesis of genetic networks

2015-01-04T17:28:59Z

MajaRemskar: /* Results */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by “On” or “Off”. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal “on” value in each particular network was also required to be at least fourfold greater than the maximal “off” value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:28:01Z

MajaRemskar: /* Results */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for ''gfp'' and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -''yfp'' transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:27:17Z

MajaRemskar: /* Explanation of results */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which “on” and “off” states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:26:53Z

MajaRemskar: /* Explanation of results */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different “topologies” can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:26:22Z

MajaRemskar: /* Results */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines “on” and “off” states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to “leaky” or “fuzzy” logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:24:43Z

MajaRemskar: /* Results */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. Logical circuit behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:23:34Z

MajaRemskar: /* Logic circuits */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:23:18Z

MajaRemskar: /* Transcriptional regulators */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the [http://www.web-books.com/MoBio/Free/Ch4H2.htm cI gene], which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example,[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:21:34Z

MajaRemskar: /* Transcriptional regulators */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

[http://www.web-books.com/MoBio/Free/Ch4H2.htm cI] is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example,[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:19:07Z

MajaRemskar: /* References */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example,[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:13:53Z

MajaRemskar: /* References */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example,[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==
1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:10:40Z

MajaRemskar: /* Discussion */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example,[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? Boolean-type models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc. [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==

1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:10:02Z

MajaRemskar: /* Logic circuits */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example,[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NAND gate] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

[http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth NOR gate] stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? ''Boolean-type'' models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: ''gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc.'' [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==

1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:08:15Z

MajaRemskar: /* Introduction */

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, NAND gate [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

NOR gate stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? ''Boolean-type'' models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: ''gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc.'' [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==

1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:07:48Z

MajaRemskar:

[http://nemenmanlab.org/~ilya/images/9/99/Guet-etal-02.pdf Article] that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, NAND gate [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

NOR gate stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? ''Boolean-type'' models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: ''gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc.'' [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==

1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:03:45Z

MajaRemskar: /* References */

Article that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, NAND gate [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

NOR gate stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? ''Boolean-type'' models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: ''gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc.'' [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==

1. Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.

2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.

3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.

4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation

5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor

6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon

7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.

8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR

9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.

10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor

11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.

12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.

13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.

14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.

15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.

16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth

17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module

18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29

19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype

20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.

21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.

22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.

23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.

24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.

25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.

26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling

27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.

28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing

29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T17:00:57Z

MajaRemskar: /* Logic circuits */

Article that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, NAND gate [http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth] which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

NOR gate stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? ''Boolean-type'' models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: ''gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc.'' [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==

↑ Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.
2. Szathmary E., Jordan F., Pal C. Molecular biology and evolution. Can genes explain biological complexity? Science, 2001, vol. 292(5520) , p. 1315-1317.
3. Guet, C.C., et al. Combinatorial synthesis of genetic networks. Science, 2002, vol. 296 (5572), p. 1466-1470.
4. Transcriptional regulation - Wikipedia, the free encyclopedia [online]. 13.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Transcriptional_regulation
5. Lac repressor - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Lac_repressor
6. Operon - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015] http://en.wikipedia.org/wiki/Operon
7. Orth, P., et al. Structural basis of gene regulation by the tetracycline inducible Tet repressor-operator system. Nature Structural Biology, 2000, vol. 7(3), p. 215-219.
8. TetR - Wikipedia, the free encyclopedia [online]. 30.11.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/TetR
9. Stayrook, S., et al. Crystal structure of the lambda repressor and a model for pairwise cooperative operator binding. Nature, 2008, vol. 452(7190), p. 1022-1025.
10. Lambda repressor - Proteopedia, life in 3D [online]. 1.1.2015. [cited 1.1.2015]. http://proteopedia.org/wiki/index.php/Lambda_repressor
11. "Analysis of Biological Networks: Transcriptional Networks - Promoter Sequence Analysis". Tel Aviv University. Retrieved 30 December 2012.
12. Elowitz, M.B., Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, vol. 403(6767), p. 335-338.
13. Mullinax, R.L., et al. Expression of a heterodimeric Fab antibody protein in one cloning step. Biotechniques, 1992, vol. 12(6), p. 864-869.
14. Berger, S.L. Expanding the potential of restriction endonucleases: use of hapaxoterministic enzymes. Analytical Biochemistry, 1994, vol. 222(1), p. 1-8.
15. Lutz, R., Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 1997, vol. 25(6), p. 1203-1210.
16. Basic Logic Gates [online]. 8.12.2005. [cited 1.1.2015]. http://www.ee.surrey.ac.uk/Projects/CAL/digital-logic/gatesfunc/index.html#truth
17. Cis-regulatory module - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Cis-regulatory_module
18. Flow cytometry - Wikipedia, the free encyclopedia [online]. 17.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Flow_cytometry#Fluorescence-activated_cell_sorting_.28FACS.29
19. Phenotype - Wikipedia, the free encyclopedia [online]. 20.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Phenotype
20. Davidson, E.H. Genomic regulatory systems : development and evolution. 2001, San Diego; London: Academic Press.
21. Carroll, S.B. Endless forms: the evolution of gene regulation and morphological diversity. Cell, 2000, vol. 101(6), p. 577-580.
22. Glass, L., Kauffman S.A. The logical analysis of continuous, non-linear biochemical control networks. Journal of Theoretical Biology, 1973, vol. 39(1), p. 103-129.
23. Thomas, R., D'Ari, R., Biological feedback. 1990, Boca Raton, Fla.: CRC Press.
24. Stemmer, W.P. DNA shuffling by random fragmentation and reassembly: in vitro recombination for molecular evolution. PNAS USA, 1994, vol. 91(22), p. 10747-10751.
25. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, vol. 415(6872), p. 644-646.
26. DNA shuffling - Wikipedia, the free encyclopedia [online]. 1.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/DNA_shuffling
27. Cohen, J. How DNA shuffling works. Science, 2001, vol. 293(5528), p. 237.
28. Quorum sensing - Wikipedia, the free encyclopedia [oniline]. 18.12.2014. [cited 1.1.2015]. http://en.wikipedia.org/wiki/Quorum_sensing
29. Hartwell, L.H., et al, From molecular to modular cell biology. Nature, 1999, vol. 402(6761 Suppl), p. C47-52.

Combinatorial synthesis of genetic networks

2015-01-04T16:57:46Z

MajaRemskar: /* Logic circuits */

Article that I selected to explain was written by Guet C.C., Elowitz M.B., Hsing W. and Leibler S. and published in Science in 2002. It seems like it is quite important because until now it was cited 471 times.

== Introduction ==
Living cells respond to information from their environment on the basis of the interactions of a large yet limited number of molecular species that are arranged in complex cellular networks [1-3]. However, despite growing knowledge about the molecular components of the cell, the dynamics of even simple cellular networks are not well understood. A central problem in biology is determining how genes interact as parts of functional networks. Creation and analysis of synthetic networks, composed of well-characterized genetic elements, provide a framework for theoretical modeling. Simple and modular experimental systems are needed to study how the genetic structure and connectivity of cellular networks are related to their function. To this end, an in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was devised in ''E. coli''. These networks were composed of genes encoding the three well-characterized prokaryotic transcriptional regulators LacI, TetR, and lambda CI, as well as the corresponding promoters [3].

Transcription is the first step of gene expression, in which a particular segment of DNA is copied into RNA by the enzyme RNA polymerase. Regulation of transcription is highly controlled process and there are plenty of different molecules involved. Transcriptional regulators control the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA. A single gene can be regulated in a range of ways, from altering the number of copies of RNA that are transcribed, to the temporal control of when the gene is transcribed. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus respond adequately. For example, mRNA is produced to encode enzymes to adapt to a change in a food source, producing the gene products involved in cell cycle specific activities, and producing the gene products responsible for cellular differentiation in higher eukaryotes [4].

== Transcriptional regulators ==
Transcriptional regulators are transcription factors (TF) and other proteins working in concert to finely tune the amount of RNA being produced through a variety of mechanisms [4]. The fact is that any given gene is likely controlled by a specific combination of factors, which is called combinatorial control. In a hypothetical example, the factors A and B might regulate a distinct set of genes from the combination of factors A and C. This combinatorial nature extends to complexes of far more than two proteins, and allows a very small subset (less than 10%) of the genome to control the transcriptional program of the entire cell [4]. All three of TFs used in this experiment are shortly described below.

LacI is lac repressor which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria. These genes are repressed when lactose is not available to the cell, ensuring that the bacterium only invests energy in the production of machinery necessary for uptake and utilization of lactose when lactose is present. When lactose becomes available, it is converted into allolactose, which inhibits the lac repressor's DNA binding ability. Loss of DNA binding by the lac repressor is required for transcriptional activation of the operon [5]. Operon is a functioning unit of genomic DNA containing a cluster of genes under the control of a single promoter [6].

The tetracycline repressor (TetR) regulates the most abundant resistance mechanism against the antibiotic tetracycline in gram-negative bacteria. The TetR protein and its mutants are commonly used as control elements to regulate gene expression in higher eukaryotes [7]. In the absence of tetracycline, basal expression of TetR is very low, but expression rises sharply in the presence of tetracycline through a positive feedback mechanism. TetR is used in artificially engineered gene regulatory networks because of its capacity for fine regulation [8].

cI is a transcription inhibitor of bacteriophage Lambda, also known as Lambda Repressor. cI is responsible for maintaining the lysogenic life cycle of phage Lambda. This is achieved when two repressor dimers bind cooperatively to adjacent operator sites on the DNA. The cooperative binding induces repression of the cro gene and simultaneous activation of the cI gene, which code for proteins Cro and cI [9, 10].

== Generating combinatorial libraries ==
So, a combinatorial library was generated composed of those three transcriptional regulatory genes and their corresponding promoters with varying connectivity. The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D-thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc) [3]. Inducer is a molecule that starts gene expression. It can bind to repressors or activators.

They also chose five promoters regulated by these proteins, which cover a broad range of regulatory characteristics such as repression, activation, leakiness, and strength [3]. Promoter is a region of DNA that initiates transcription of a particular gene. Promoters are located near the transcription start sites of genes, on the same strand and upstream on the DNA. They can be about 100–1000 base pairs long [11]. Two of the promoters are repressed by LacI, one is repressed by TetR, and the remaining two are regulated by λ cI, one positively and one negatively.

The genetic assembly scheme they used ensures that each network in the library has the following structure: Pi-lacI—Pj-λcI—Pk-tetR, wherein each Pi, Pj, Pk represents any of the five promoters (Fig. 1). The regulatory genes on each plasmid activate or repress one another in various ways in vivo, generating networks with diverse connectivities. Altogether, 53 = 125 different networks are possible [3].

=== Methods ===
In Figure 1, they present modular genetic cloning strategy used to generate combinatorial libraries of logical circuits. At first (A) all 15 possible promoter-gene units were built. Individual promoters and genes were than amplified by PCR. The genes [denoted ''-lite'' in (B)] have an ssrA tag that reduces the half-life of the proteins encoded by the modified gene [12]. The five promoters used were PL1 and PL2 (repressed by LacI), PT (repressed by TetR), and Pλ - and Pλ+ (repressed and activated by λ cI). The transcriptional terminator T1 was present at the end of each gene [3].

They amplified promotor region and separately the region with the gene. Identical RBS sites were used as internal primers for the subsequent fusion PCR step to form promoter-gene units [13]. In order to control the number of promoter-gene units and the position of a given gene in the network, Bgl I sites were incorporated in PCR primers, as shown. The special recognition and restriction properties of Bgl I [14] allow various sticky ends to be produced by Bgl I cleavage. Here, they designed the Bgl I sites such that specific cohesive ends x and y were associated with each regulatory gene (for lacI, xlac = GCC, ylac = TTC; for λ cI, xcI = AAG, ycI = GTG; and for tetR, xtet = CAC, ytet = TCG). You can notice that ylac is compatible with xcI, ycI is compatible with xtetR, and so on [3].

In step B when all 15 possible fusion PCR products were mixed together and ligated, the resulting products contained exactly three promoter-gene units in one particular order (lacI, λ cI, tetR). These products were cloned into a low copy number plasmid (3-4 copies/cell) [15], carrying the reporter gene ''gfpmut3'' under the control of Pλ- , which is a fourth transcriptional unit coding for green fluorescent protein (GFP) controlled by the λ cI repressible promoter. The fluorescent signal acts as the network output, whereas the levels of the two chemical inducers were used as inputs [3].

The plasmid library was transformed into two different host strains of ''E. coli'', CMW101 (lacI-, tetR-) and DH10β (lacI+, tetR-), which differed most significantly by the presence of a wild-type copy of lacI at a chromosomal locus. Each clone was grown under four conditions, with and without IPTG and with or without aTc. GFP fluorescence was monitored simultaneously during cell growth. In this way, they searched the library for circuits in which the output is a binary logical function of both inducers. Examples of such logical circuit are NAND, NOR, or NOT IF [3].

== Logic circuits ==
Logical circuits are based on Boolean function, which performs a logical operation on one or more logical inputs, and produces a single logical output. In our case circuits are binary, so there are exactly two logical inputs.

For example, NAND gate which stands for NOT-AND gate and is equal to an AND gate followed by NOT gate. The outputs of all NAND gates are high if any of the inputs are low. The symbol is an AND gate with a small circle on the output that represents inversion [16].

NOR gate stands for NOT-OR gate which is equal to an OR gate followed by a NOT gate. The outputs of all NOR gates are low if any of the outputs are high. The symbol is an OR gate with a small circle on the output. The small circle represents inversion [16].

=== Results ===
Figure 2 presents detailed analysis of two binary logical circuits (D038 and D052). (A) Here we have two host strains of ''E. coli'' (CMW101 and DH10β) transformed with each of two networks. ''Logical circuit'' behavior can be observed directly on agar plates, where fluorescence of colonies defines ''on'' and ''off'' states. Cells containing the indicated network were patched onto minimal agar media containing all four combinations of the two inducers. To increase fluorescent signal and to show that reporter expression is not cis-dependent, cells contained plasmids deleted for gfp and were co-transformed with compatible plasmids (~15 copies/cell) containing an equivalent Pλ- -yfp transcriptional unit. So the reporter unit was on the other plasmid [3].

To explain what means not being cis-dependent, I will take the example of cis-regulatory module. This is a stretch of DNA, where the number of TF can bind and regulate expression of nearby genes and regulate their transcription rates. They are labelled as cis because they are typically located on the same DNA as the genes they control [17]. So here, they wanted to make sure that expression of repressor is not dependent on the proximity of genes coding for TFs and the reporter can still work in spite of not being located on the same DNA strand.

Cells were also grown in liquid culture and populations were analyzed with FACS for distributions of GFP expression. FACS or fluorescence-activated cell sorting is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell [18]. In each set of histograms, the blue curve shows the fluorescence distribution without inducers, the green curve shows when IPTG alone was present, the red curve indicates aTc alone, and the cyan curve shows the distribution when both inducers were present simultaneously. Single-peaked distributions are observed, but, in some cases, peaks contain long tails that overlap, corresponding to ''leaky'' or ''fuzzy'' logical circuits. (B) Sequencing was used to determine the connectivity of each of the two networks. Their logical behavior is different but both networks have the same connectivity, as can be inferred from the corresponding diagrams of the interactions between the repressors and promoters. The schematic connectivity or topology diagrams shown at the bottom are identical for the two networks [3].

In Figure 3, we can see distribution of logical phenotypes in the two strains. (A) Definition of the logic operations performed by the circuits. In the top row, + and - indicate the presence or absence of each inducer input. The output (fluorescence) is indicated in the lower rows by ''On'' or ''Off''. We do not distinguish here between the two inputs and, thus, between two different types of NOT IF logic functions. Colored bars act as legends for (B) and (C). Histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter. A single universal threshold value was applied simultaneously to all networks. Besides being greater than this threshold, the minimal ''on'' value in each particular network was also required to be at least fourfold greater than the maximal ''off'' value [3].

Figure 4. Genetic structure and behavior of selected networks. A subset of 30 plasmids was characterized in the lac- host strain (CMW101), and their genetic composition was determined by sequencing. Levels of GFP expression in each of the four conditions are indicated by the color or intensity of the corresponding box on a linear intensity scale (see color bar). In many cases, logical behavior is strain dependent (i.e., is different in lac+ strain DH10β). The promoters incorporated in the network, determine its connectivity diagram. The 13 connectivity diagrams corresponding to different networks are drawn. A and B refer to either of the inducible repressors (LacI and TetR), C always denotes λ cI, and G denotes GFP. Activation is denoted by sharp arrows (↓), while repression is denoted by blunt arrows (┴) [3].

Figure 5. Dependence of phenotypic behavior on network connectivity. (A) A single change of the promoter can completely modify the behavior of the logical circuit. The network on plasmid D133, which is always in the ''on'' state, differs from the networks encoded in plasmids D038 and D016 by exchange of single promoter placed in front of lac gene (shown in red). Both circuits show diverse logical behaviors, which differ in the two bacterial host strains. (B) Networks can differ by their connectivity but have qualitatively the same logical function. Both, D016 and D052, behave as NOR logical circuits (in both lac+ and lac- strains), but they have different connectivity, as shown schematically on the right side of the table. Intensity scale indicating the levels of GFP expression is the same as in Fig. 4 [3].

== Explanation of results ==
In many cases, the output fluorescence levels of an individual culture in their library were sufficiently distinct for different inputs that an unambiguous binary output value for each input state could be assigned without any problem. In other cases, the designation was somewhat arbitrary [3]. There could be a single universal output, in this case fluorescence threshold, on the entire library and can still be obtained a large number of logical circuits for which ''on'' and ''off '' states differ significantly (Fig. 3). Naturally, spectrum of logical behaviors differed in the two hosts (Fig. 3) because the chromosomal lacI gene present in one strain (DH10β) acts as a network component and thus may drastically change the resulting phenotype (Fig. 2A) [3]. A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behavior, etc. [19].

Phenotypic variation in organisms often arises through mutations in the protein coding regions of the DNA. Another important contribution to phenotypic variability comes from changes in the cis-regulatory connections of existing genetic elements [3, 20, 21]. To determine the origin of the phenotypic variability observed in the library, 30 clones with a variety of different behaviors were retransformed into both hosts, rescreened, and sequenced (Fig. 4). There was a low level of point mutations, which, in some cases, modify the logical behavior of the networks. However, a large variety of behaviors remains among the many networks that do not have mutations in their regulatory regions. By sequencing they identified the three promoters incorporated in each plasmid; connectivity between different genetic elements varies from network to network so that 13 different ''topologies'' can be distinguished among the sequenced networks (Fig. 4). This variety of network connectivity is evidently the major source of phenotypic diversity in the library. In fact, the sequence data show that single step changes to the network connections, in which one promoter replaces another, frequently converted network operation from one logical function to another [3]. When they replaced a single promoter in a network (D133 in Fig. 5A), which is always in the “on” state, one obtains a network (D038), which acts as a NOT IF in one strain and as a NAND in the other. Alternatively, by performing a different promoter replacement, one obtains network D016, which acts as a NOR circuit in both strains [3].

Just one-step change in connectivity and a set of genes and promotor has the potential to switch among a variety of different computational functions. Once a simple set of genes and cis-regulatory elements is in place, it should be possible to jump from one functional phenotype to another just by modifying the regulatory connections. Such changes can also be achieved in evolution by natural combinatorial mechanisms like transposition, recombination, or gene duplication [3] but the process in much slower if not caused by effects driven by successive point mutations.

The system is even more complex because connectivity of a network does not uniquely determine its behavior. Some networks that share the same connectivity but perform different logical operations show very different phenotypic behavior (Fig. 2). There are also examples of networks with different connectivity that exhibited qualitatively similar behaviors. For instance, two networks shown in Fig. 5B (D016 and D052) both perform NOR operations despite their different connectivity. So the behavior of even simple networks built out of a few, well-characterized components cannot always be inferred from connectivity diagrams alone [3].

== Discussion ==
The question is if the behavior of the logical circuits obtained here can be predicted? ''Boolean-type'' models of gene regulation are often used to intuitively understand the operation of genetic networks where only discrete values of the biochemical variables and parameters are considered. That leads to commonly used reasoning, such us: ''gene product A is produced, it inhibits the expression of gene product B, which is thus absent, etc.'' [22, 23]. This description is adequate for some of the present networks but seems not to apply to others. Boolean-type models neglect many potentially important intracellular phenomena, including stochastic fluctuations in the levels of components and the detailed biochemistry of protein-DNA interactions.

They determined all Boolean network structures theoretically possible in the library for lac- strain CMW101. Only three network structures were expected to depend on both inputs, and they all behaved as NOT IF. However, experimentally they also found NORs. As shown in Fig. 2, , the Boolean description is consistent with the NOT IF behavior of network D038 but not the NOR behavior of network D052. It is possible that even for these well-studied transcriptional regulators subtle additional regulation may be at work among the plasmid-encoded elements [3]. Genetic networks are nonlinear, stochastic systems in which the unknown details of interactions between components might be of crucial importance. Combinatorial libraries of simple networks should be useful in the future to uncover the existence of such additional regulation mechanisms and to explore the limits of quantitative modeling of cellular systems [3].

Combinatorial techniques inspired by recombination, such as DNA shuffling, have often proven successful in enhancing or changing the enzymatic activities of proteins and pathways [24, 25] without requiring an understanding of the mechanisms by which they work. DNA shuffling is a way to rapidly propagate beneficial mutations in a directed evolution experiment [26]. It is used to rapidly increase DNA library size [27] because it is a recombination between different DNA species with different mutations [26]. However, combinatorial methods in simple and well-controlled systems can and should also be used to gain better understanding of system and level properties of cellular networks for further practical applications [3].

The present results show that really little of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. The current system uses a small number of building blocks restricted only to transcriptional regulation. Both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements [3].
There are also some ideas for the future. The approach can be taken beyond the intracellular level by linking input and output through cell-cell signaling molecules, such as those involved in quorum sensing [3]. The latter is a system of stimulus and response correlated to population density. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population [28]. Lastly, this combinatorial strategy can be used to search for other dynamic behaviors such as switches, sensors, oscillators, and amplifiers, as well as for high-level structural properties, such as robustness or noise-resistance [29].

== References ==

↑ Bray, D. Protein molecules as computational elements in living cells. Nature, 1995, vol. 376(6538), p. 307-312.
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NAND gate

2015-01-04T16:57:10Z

MajaRemskar: Removing all content from page

MBT seminarji 2014

2014-03-06T23:07:05Z

MajaRemskar:

'''Seznam seminarjev iz Molekularne biotehnologije v študijskem letu 2013/14'''

V študijskem letu 13/14 izvajamo predmet Molekularna biotehnologija (in s tem tudi seminarje) prvič.
Tabela za razpored po tednih bo objavljena v spletni učilnici, vanjo pa se vpišite tudi za kratke predstavitve novic (5 min). Na tej strani bo samo seznam odobrenih člankov za seminar in povezave do člankov in do povzetkov, ki jih morate objaviti najkasneje tri dni pred predstavitvijo (petek).

Način vnosa:

# The importance of ''Arabidopsis'' glutathione peroxidase 8 for protecting ''Arabidopsis'' plant and ''E. coli'' cells against oxidative stress (A. Gaber; GM Crops & Food 5(1), 2014; http://dx.doi.org/10.4161/gmcr.26979) Pomen glutation peroksidaze 8 iz repnjakovca za zaščito rastline ''Arabidopsis thaliana'' in bakterije ''Escherichia coli'' pred oksidativnim stresom. Janez Novak, 15. marca 2014
(slovenski naslov povežete z novo stranjo, na kateri bo povzetek)

Naslovi odobrenih člankov:

# Generation of protective immune response against anthrax by oral immunization with protective antigen plant-based vaccine. Sabina Kolar
# Influence of valine and other amino acids on total diacetyl and 2,3-pentanedione levels during fermentation of brewer’s wort. Jernej Mustar
# Development of influenza H7N9 virus like particle (VLP) vaccine: Homologous A/Anhui/1/2013 (H7N9) protection and heterologous A/chicken/Jalisco/CPA1/2012 (H7N3) cross-protection in vaccinated mice challenged with H7N9 virus (G. E. Smith ''et al''.; Vaccine 31, 4305-4313, 2013; http://www.sciencedirect.com/science/article/pii/S0264410X13009870). Razvoj cepiva za virus gripe H7N9 na osnovi virusu podobnih delcev ter primer uporabe cepiva pri miših. Ana Dolinar
# Generation of tumor-targeted human T lymphocytes from induced pluripotent stem cells for cancer therapy (M. Themeli ''et al.''; Nature Biotechnology 31, 928–933, 2013; http://www.nature.com/nbt/journal/v31/n10/full/nbt.2678.html). [[Iz induciranih pluripotentnih izvornih celic pripravljeni človeški limfociti T za terapijo raka]]. Urban Bezeljak
# Negative selection and stringency modulation in phage-assisted continuous evolution (Jacob C. Carlson, Ahmed H. Badran, Drago A. Guggiana-Nilo & David R. Liu; Nature chemical biology 10, 216–222, 2014; http://www.nature.com/nchembio/journal/v10/n3/full/nchembio.1453.html) Negativna selekcija in spreminjanje striktnosti pri zvezni evoluciji s pomočjo fagov. Valter Bergant
# ... Vito Frančič
# Engineering Escherichia coli for selective geraniol production withminimized endogenous dehydrogenation (http://www.sciencedirect.com/science/article/pii/S016816561300494X)Maja Remškar
# ... Veronika Jarc
# ... Špela Podjed
# Transcriptional comparison of the filamentous fungus Neurospora crassa growing on three major monosaccharides D-glucose, D-xylose and L-arabinose (J. Li ''et al''.; Biotechnology for Biofuels 7:31, 2014; http://www.biotechnologyforbiofuels.com/content/7/1/31/abstract). Luka Bevc
# Xylanase and cellulase systems of Clostridium sp.: An insight on molecular approaches for strain improvement (L. Thomas ''et al''.; Bioresource Technology 2014 - v tisku; http://dx.doi.org/10.1016/j.biortech.2014.01.140) Luka Grm
# Consolidated conversion of protein waste into biofuels and ammonia using Bacillus subtilis (K-Y. Choi ''et al''.; Metabolic Engineering 2014 - v tisku; http://dx.doi.org/10.1016/j.ymben.2014.02.007). Elmina Handanović
# A plant factory for moth pheromone production (B-J. Ding ''et al''.; Nature Communications 5, 3353, 2014; http://www.nature.com/ncomms/2014/140225/ncomms4353/full/ncomms4353.html). Filip Kolenc
# Introduction of the rd29A:AtDREB2A CA gene into soybean (Glycine max L. Merril) and its molecular characterization in leaves and roots during dehydration (C. Engels ''et al''.; Genetics and Molecular Biology 36(4): 556–565, 2013; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3873188/) Aleksander Krajnc
# Isolation and characterization of formaldehyde-degrading fungi and its formaldehyde metabolism (D. Yu ''et al''.; Environmental Science and Pollution Research 2014 - v tisku; http://dx.doi.org/10.1007/s11356-014-2543-2) Sara Sajko
# Enantioselective lactic acid production by an Enterococcus faecium strain showing potential in agro-industrial waste bioconversion: Physiological and proteomic studies (A. Pessione ''et al''.; Journal of Biotechnology 173, 31–40, 2014; http://dx.doi.org/10.1016/j.jbiotec.2014.01.014) Žan Železnik

Forenzika, Determining the Body Fluid Origin

2013-12-13T22:01:34Z

MajaRemskar:

=='''UVOD'''==
Mikro-RNA so majhne nekodirajoče verige RNA, ki negativno regulirajo (zavirajo) izražanje genov na stopnji translacije. Molekule dolge le od 21-25 nukleotidov imajo v forenzični znanosti velik pomen. V celici imamo okoli 50.000 verig posamezne miRNA, torej jih je res v izobilju, poleg tega pa so zaradi svoje dolžine manj dovzetne za razgradnjo. Pomemben je podatek, da so miRNA tkivno specifične.

miRNA se specifično izraža v za forenziko pomembnih telesnih tekočinah, npr. v krvi, slini. Cilj študije je bilo razviti postopek, s katerim bi hkrati izolirali miRNA in DNA. Z analizo bi dobili DNA-profil osebe ter tudi identificirali telesno tekočino, iz katere so DNA pridobili.

Dosedanja praksa je temeljila na standardnem ločevanju DNA in mRNA faze. Tukaj pa so želeli doseči izvedbo izolacije in analize brez vmesne separacije DNA in miRNA. Rezultat je le en sam elektroferogram s profilom DNA pridobljene iz telesne tekočine in tudi identifikacija te določene telesne tekočine. S tem bi tudi zminimizirali količino telesnih tekočin potrebne za določen test. S študijo so želeli le dokazati, da je ta koncept izvedljiv. Na ta način sta ta dva podatka neposredno povezana, kar ima velik pomen za forenziko.
=='''METODE'''==
DNA in miRNA so izolirali iz krvi in sline. Sledila je cDNA sinteza z uporabo PCR s stebelno zanko s sledečo reverzno transkripcijo. Ekstrakte DNA in cDNA, sintentizirane iz markerjev miRNA specifičnih za določeno telesno tekočino, so analizirali za STR (mikrosateliti oz. kratke tandemske ponovitve) z malce nadgrajenim ABI AmpFlSTR® NGM SElect™ kompletom reagentov. V kompletu reagentov so bili začetni oligonukleotidi za pomnoževanje 17 lokusov. Pri vseh vzorcih je bil prisoten popoln profil DNA z dodatnimi vrhovi miRNA markerja. V vseh primerih so vzorci krvi kazali vrh odgovarjajoč pristotnosti za kri specifičnega miRNA markerja, prav tako je vrh vzorca sline kazal na prisotnost miRNA markerja za slino. Smerna začetna oligonukleotida obeh miRNA sta bila dodatno označena še s florescentnimi barvili za lažjo zaznavo.

Analiza fragmentov je bila nato izvedena s kapilarno elektoforezo. Z njo so zaznali prisotnost določenega amplikona miRNA.
=='''REZULTATI IN RAZPRAVA'''==
Cilj študije je bil razviti test hkratne analize DNA in miRNA, kar so dosegli z modifikacijo že obstajajočega testa za profiliranje DNA, ki vsebuje tudi DNA začetne oligonukleotide iz Evropske standardne zbirke. V postopku so dodatno uporabili začetne oligonukleotide za miRNA iz vzorcev krvi in sline ter korak cDNA sinteze.

Kri in slino je darovalo pet prostovoljcev. Pri vseh petih se je DNA-profil ujemal z referenčnim vzorcem njihove DNA, prav tako je bila telesna tekočina pravilno identificirana. V vseh primerih so imeli 100% uspeh.
Pozitivne kontrole so bile pričakovanih velikosti. Pri negativnih kontrolah ni prišlo do pomnoževanja miRNA.
Skrbel jih je korak reverzne transkripcije, zaradi možnosti vključitve v vzorcu prisotne DNA. To so ovrgli, saj so bile negativne kontrole prazne, ni prišlo do amplifikacij. Ko pa so negativne kontrole reverzne transkripcije dali na kapilarno elekroforezo, so dobili le profil človeške DNA. Na elektoferogramu ni bilo prisotnih prav nič amplikonov miRNA, kar kaže na pravilno karakterizacijo miRNA.

Zanimala jih je tudi meja detekcije miRNA. Naredili so serijske redčitve, in sicer so začetno koncentracijo DNA vsakokrat redčili za faktor 10. miRNA so lahko detektirali tudi pri koncentraciji 1 pg/μl DNA. Standard pri DNA analizi je koncentracija od 0,5-1 ng/μl, kar pomeni, da bi v primerih, kjer ni možno napraviti celotnega profila DNA, vseeno lahko identificirali vsaj za katero telesno tekočino gre.

V nekaterih primerih je ključno potrditi ali je analizirana DNA prišla iz krvi ali npr. iz sline. Če imajo le sledi krvi, ne morejo zatrdno trditi ničesar. S to metodo lahko identifikacijo telesne tekočine povežejo trdno skupaj z DNA-profilom določene osebe.
V prihodnosti si želijo svoj opus specifičnih miRNA markerjev razširiti. Radi bi znali določiti tudi spermo, vaginalni material, menstrualno kri in venozno kri.

=='''Članek'''==
Van der Meer, D., Uchimoto, M. L., Williams, G. Simultaneous Analysis of Micro-RNA and DNA for Determining the Body Fluid Origin of DNA Profiles. Journal of Forensic Science, 2013, letn. 58, št. 4, str. 967-971 (doi: 10.1111/1556-4029.12160)