Combinatorial Synthesis of Genetic Networks Calin C. Guet, Michael B. Elowitz, Weihong Hsing, Stanislas Leibler Amit Meshulam Bioinformatics Seminar Technion, Spring 06

Combinatorial Synthesis of Genetic Networks Phenomena description and biological background Biological system description Construction of combinatorial libraries and genetic engineering techniques Description and Analysis of experiments results Summary Remarks

Combinatorial Synthesis of Genetic Networks Phenomena description and biological background Biological system description Construction of combinatorial libraries and genetic engineering techniques Description and Analysis of experiments results Summary Remarks

Phenomena description and biological background Complex pathways occur in the cell, including interactions between biological element Biological elements such as: proteins, chemical molecules, DNA fragments etc.. predict the cell behaviorThe goal is to predict the cell behavior, in various growth conditions, under the activation of signals etc..

Phenomena description and biological background (cont) Live cells react to inputs from the environment. The reactions are based on interactions between big number of molecules types organized as complex network cells. A central problem in biology is determining how genes interact as parts of functional networks. Biological network analysis – mapping of inter-genes interactions in specific organism.

Phenomena description and biological background (cont)

Gene expression and regulation mechanism PromoterExons DNA Enhancer Regulator Protein

Example - Inter biological elements interactions (Ecoli)

Example of biological network

Combinatorial Synthesis of Genetic Networks Phenomena description and biological background Biological system description Construction of combinatorial libraries and genetic engineering techniques Description and Analysis of experiments results Summary Remarks

Biological system description The genetic structure and cell networks is required in order to analyze the cell behavior. An in vivo synthetic system that enables the generation of combinatorial libraries of genetic networks was created. The networks exhibit a large variety of connectivity of E.coli.

Biological system description (cont) 3 well-characterized prokaryotic transcriptional regulators were chosen: - LacI - TetR - lambda cI The binding state of LacI and TetR can be changed with the small molecule inducers, isopropyl b-D- thiogalactopyranoside (IPTG) and anhydrotetracycline (aTc), respectively: IPTG - IPTG – The inducer that binds to the LacI protein and prevent the binding to the target DNA. aTc - aTc – The inducer that binds to the TetR protein and prevent the binding to the target DNA.

Biological system description (cont) 5 promoters regulated by these proteins, covering a broad range of regulatory characteristics such as repression, activation, leakiness, and strength were chosen: - 2 promoters repressed by LacI - 1 repressed by TetR - 1 regulated by lambda cI: 1 positively and 1 negatively.

Biological system description (cont) Any network in the library will form the following configuration: P i, P j and P k represent one of the 5 promoters selected for the system. Each promoter has 5 options resulting in 5*5*5 = 125 optional networks Pi lacI Pj Lambda cI Pk tetR

Biological system description (cont) The encoding gene to the florescent protein (GFP), was added downstream to the promoter repressed by lambda cl. The fragment is transformed into two different host strains of E. coli PilacIPjLambda cIPktetR PcI GFP

Biological system description (cont) Network input: - X and Y Booleans: X – true if IPTG inducer was added, false otherwise. Y – true if aTc inducer was added, false otherwise. Network output: various levels of florescent signal reflecting the expression level of the protein GFP.

GFP protein as biological indicator GFP - Green Fluorescent Protein. The gene transformation into cells organisms

Combinatorial Synthesis of Genetic Networks Phenomena description and biological background Biological system description Construction of combinatorial libraries and genetic engineering techniques Description and Analysis of experiments results Summary Remarks

Combinatory library construction Using modular genetic cloning strategy generating combinatorial libraries of logical circuits. Construction of the library proceeded in two steps Step 1 – Creating DNA fragments. Every DNA fragment is constructed from the fusion between one of the 5 promoters with one of the 3 proteins. 3*5 = 15 different fragments. Step 2 – Fusion of all fragments in the right order, insertion of the fragment into the plasmid and transformation of the plasmid into the hosting cell.

Combinatory library construction: Step 1 - Amplification of the promoters and the genes by PCR technique. - Every gene has a transcription terminator. - At the end of every promoter and the beginning of every gene an identical RBS was added by PCR. (RBS = Ribosome Binding Site) - In order to control the number and the insertion direction of the fragments to the plasmid a DNA fragment was inserted. - This fragment include restriction site of the restriction enzyme (BglI) and was inserted upstream to the promoter and downstream to the gene. - Sticky ends are created once cutting the restriction enzyme. - After ligation the sticky ends fused to each other to create the required fragment.

Step 1: Network component constructions (Fragment containing gene & promoter)

technique PCR

5’ 3’

Step 2: In-order fragment fusion Step 1 products are cloned into the plasmid according to the required order. PilacIPjLambda cIPktetR PcI GFP

Step 2: In-order fragment fusion How to ensure the in-order fragments fusion?How to ensure the in-order fragments fusion? Restriction site of the Bgl I (pre-restriction): Post-restriction: ATTGCCATCGGNNNNNCCGTCGCAAT TAACGGTAGCCNNNNNGGCAGCGTTA TAACGGTAGCCNNNN ATTGCCATCGGN NGGCAGCGTTA NNNNCCGTCGCAAT

Step 2: In-order fragment fusion YY represents the restriction site fragment fused downstream the gene of fragment A. XX represents the restriction site fragment fused upstream the gene of fragment B. TAACGGTAGCCNNNN ATTGCCAT CGGN Y Gene APa NGGCAGCGTT NNNNCCGTCGCAAT X PbGene B PaGene APbGene B

Step 2: In-order fragment fusion The characterization of the fusion sites: - Y lacI complimentary to X cl - Y cl complimentary to X tetR etc.. Shuffling of all fragments.

Insertion the resulting fragment into a plasmid Plasmid restriction by restriction enzyme in the right position. Fragment insertion into the plasmid:

Transformation into hosting cell The plasmids transformed into 2 hosting E.coli strains (3-4 copies) - lacI + (wt) - lacI - Every clone was grown in different conditions: IPTGaTc

Combinatorial Synthesis of Genetic Networks Phenomena description and biological background Biological system description Construction of combinatorial libraries and genetic engineering techniques Description and Analysis of experiments results Summary Remarks

Introducing & analysis of specific binary logical circuit To the 2 clones lacI + and lacI- the following network was inserted:

Introducing & analysis of specific binary logical circuit 2 of the strains were raised on agar plat in those conditions. The following fluorescents outputs were received:

Scenario demonstration Input: IPTG – aTc +

tetR Origin aTc PtlacIPlLambda cIPttetRPcIGFP tetR aT c tetR Origin aTc

tetR Origin aTc PtlacIPlLambda cIPttetRPcIGFP tetR aT c tetR Origin aTc

lacI PtlacIPlLambda cIPttetRPcIGFPPtlacIPlLambda cIPttetRPcIGFP tetR aT c tetR Origin aTc

lacI PtlacIPlLambda cIPttetRPcIGFP tetR Origin aTc PtlacIPlLambda cIPttetRPcIGFP tetR aT c tetR Origin aTc

cI GFP PtlacIPlLambda cIPttetRPcIGFP

Graphical representation

tetR lacI Origin cI GFP aTc tetR Origin

lacI Origin cI GFP aTc

lacI Origin cI GFP aTc

lacI Origin GFP aTc

lacI Origin GFP aTc

Scenario demonstration Input: IPTG – aTc –

tetR PtlacIPlLambda cIPttetRPcIGFP tetR Origi n

tetR PtlacIPlLambda cIPttetRPcIGFP lacI-lacI + tetR Origi n

tetR PtlacIPlLambda cIPttetRPcIGFP lacI-lacI + lacI Origi n From the origin gene Pl Lambda cI PttetR PcI GFPPl Lambda cI PttetR PcI GFP lacI tetR Origi n

tetR PtlacIPlLambda cIPttetRPcIGFP lacI-lacI + lacI Origi n From the origin gene Pl Lambda cI PttetR PcI GFPPl Lambda cI PttetR PcI GFP lacI tetR Origi n

GFP Pl Lambda cI PttetR PcI GFP cI Pl Lambda cI PttetR PcI GFP cI

Graphical representation LacI+

tetR lacI Origin lacI cI GFP tetR Origin

lacI Origin lacI cI GFP tetR Origin

lacI cI GFP tetR Origin

lacI cI GFP tetR Origin

lacI GFP tetR Origin

lacI GFP tetR Origin

Graphical representation LacI-

tetR lacI cI GFP tetR

lacI cI GFP tetR

cI GFP tetR

cI GFP tetR

cI tetR

FACS analysis The experiment was repeated in a fluid medium. The output was analyzed by FACS. FACS is an innovative equipment enabling to separate aggregation of cells according to the florescent transmission specific to cell type.

FACS analysis

X axis – florescent level. Y axis – cell number. LacI - diagram presents high florescent level only for IPTG- aTc+. LacI + diagram presents low florescent level only for IPTG+ aTc+.

Network connectivity Schematic connectivity describes the relationship between the biological element in the network. Schematic connectivity or topology diagram in our example:

Logical operations in logical circuits A - Definition of the logic operations performed by the circuits. B+C - These histograms show the fraction of networks qualifying as logical circuits of each type for varying values of a threshold parameter.

Dependence of phenotypic behavior on network connectivity Is connectivity of a network uniquely determine its behavior?

Dependence of phenotypic behavior on network connectivity For example – the following tow networks have the same connectivity but different logical behavior.

Dependence of phenotypic behavior on network connectivity

Dependence of network connectivity on phenotypic behavior Is logical function uniquely determine its connectivity of network?

Dependence of phenotypic behavior on network connectivity Networks can differ by their connectivity but have qualitatively the same logical function. For example: NOR

Dependence of phenotypic behavior on network connectivity A single change of the promoter can completely modify the behavior of the logical circuit. For example: NOT IFNAND NOR

Logical Behavior of selected networks IPTG aTc NOR NOT IF NAND NOR NOT IF NOR

Combinatorial Synthesis of Genetic Networks Phenomena description and biological background Biological system description Construction of combinatorial libraries and genetic engineering techniques Description and Analysis of experiments results Summary Remarks

Conclusion Connectivity of a network does not uniquely determine its behavior. Networks can differ by their connectivity but have qualitatively the same logical function.

Summary 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.

Summary For instance, it would be interesting to see whether the behavior of all the networks in the library could be described within a single theoretical model, a model defined by a unique set of parameters characterizing the interactions between the genetic components.

Summary Combinatorial methods in simple and well- controlled systems, such as the one presented here, can and should also be used to gain better understanding of system-level properties of cellular networks. This is particularly important before using these powerful techniques more widely, e.g., in any practical applications.

Summary The present results show that a handful of interacting genetic elements can generate a surprisingly large diversity of complex behaviors. Although the current system uses a small number of building blocks restricted to a single type of interaction (transcriptional regulation), both the number of elements and the range of biochemical interactions can be extended by including other modular genetic elements.

Summary 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. 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.

Combinatorial Synthesis of Genetic Networks Phenomena description and biological background Biological system description Construction of combinatorial libraries and genetic engineering techniques Description and Analysis of experiments results Summary Remarks

Comments The article relates only to very specific networks. There are no decisive conclusions. No suggestions for generic approach.