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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

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Amit meshulam bioinformatics seminar technion spring 06

Combinatorial Synthesis ofGenetic NetworksCalin C. Guet, Michael B. Elowitz, Weihong Hsing,Stanislas Leibler

Amit Meshulam

Bioinformatics SeminarTechnion, Spring 06


Combinatorial synthesis of genetic networks

Combinatorial Synthesis ofGenetic 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 networks1

Combinatorial Synthesis ofGenetic 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

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..

  • The goal is to predict the cell behavior, in various growth conditions, under the activation of signals etc..


Phenomena description and biological background cont

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 cont1

Phenomena description and biological background (cont)


Gene expression and regulation mechanism

Regulator Protein

Enhancer

DNA

Promoter

Exons

Gene expression and regulation mechanism


Example inter biological elements interactions ecoli

Example - Inter biological elements interactions (Ecoli)


Example of biological network

Example of biological network


Combinatorial synthesis of genetic networks2

Combinatorial Synthesis ofGenetic 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

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

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 – The inducer that binds to the LacI protein and prevent the binding to the target DNA.

    - aTc – The inducer that binds to the TetR protein and prevent the binding to the target DNA.


Biological system description cont1

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 cont2

Pi

lacI

Pj

Lambda cI

Pk

tetR

Biological system description (cont)

  • Any network in the library will form the following configuration:

  • Pi, Pj and Pk represent one of the 5 promoters selected for the system.

  • Each promoter has 5 options resulting in

    5*5*5 = 125 optional networks


Biological system description cont3

Pi

lacI

Pj

Lambda cI

Pk

tetR

PcI

GFP

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


Biological system description cont4

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 protein as biological indicator

  • GFP - Green Fluorescent Protein.

  • The gene transformation into cells organisms


Combinatorial synthesis of genetic networks3

Combinatorial Synthesis ofGenetic 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

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

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

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


Technique pcr

techniquePCR


Technique pcr1

5’

3’

3’

5’

5’

3’

3’

5’

techniquePCR


Step 2 in order fragment fusion

Pi

lacI

Pj

Lambda cI

Pk

tetR

PcI

GFP

Step 2: In-order fragment fusion

Step 1 products are cloned into the plasmid according to the required order.


Step 2 in order fragment fusion1

TAACGGTAGCCNNNNNGGCAGCGTTA

ATTGCCATCGGNNNNNCCGTCGCAAT

TAACGGTAGCCNNNN

NGGCAGCGTTA

ATTGCCATCGGN

NNNNCCGTCGCAAT

Step 2: In-order fragment fusion

  • How to ensure the in-order fragments fusion?

  • Restriction site of the Bgl I (pre-restriction):

  • Post-restriction:


Step 2 in order fragment fusion2

Y

X

Pb

Gene B

Pa

Gene A

Pa

Gene A

Pb

Gene B

TAACGGTAGCCNNNN

NGGCAGCGTT

ATTGCCAT CGGN

NNNNCCGTCGCAAT

Step 2: In-order fragment fusion

  • Y represents the restriction site fragment fused downstream the gene of fragment A.

  • X represents the restriction site fragment fused upstream the gene of fragment B.


Step 2 in order fragment fusion3

Step 2: In-order fragment fusion

  • The characterization of the fusion sites:

    - YlacI complimentary to Xcl

    - Ycl complimentary to XtetR

    etc..

  • Shuffling of all fragments.


Insertion the resulting fragment into a plasmid

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

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:


Combinatorial synthesis of genetic networks4

Combinatorial Synthesis ofGenetic 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

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 circuit1

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

Scenario demonstration

Input:

IPTG –

aTc +


Amit meshulam bioinformatics seminar technion spring 06

tetR

aTc

tetR

Origin

tetR

Origin

aTc

aTc

Pt

lacI

Pl

Lambda cI

Pt

tetR

PcI

GFP


Amit meshulam bioinformatics seminar technion spring 06

tetR

aTc

tetR

Origin

tetR

Origin

aTc

aTc

Pt

lacI

Pl

Lambda cI

Pt

tetR

PcI

GFP


Amit meshulam bioinformatics seminar technion spring 06

tetR

aTc

tetR

Origin

aTc

Pt

Pt

lacI

lacI

Pl

Pl

Lambda cI

Lambda cI

Pt

Pt

tetR

tetR

PcI

PcI

GFP

GFP

lacI


Amit meshulam bioinformatics seminar technion spring 06

tetR

aTc

tetR

Origin

tetR

Origin

aTc

aTc

Pt

Pt

lacI

lacI

Pl

Pl

Lambda cI

Lambda cI

Pt

Pt

tetR

tetR

PcI

PcI

GFP

GFP

lacI


Amit meshulam bioinformatics seminar technion spring 06

Pt

lacI

Pl

Lambda cI

Pt

tetR

PcI

GFP

cI

GFP


Amit meshulam bioinformatics seminar technion spring 06

Graphical representation


Amit meshulam bioinformatics seminar technion spring 06

tetR

aTc

lacI

tetR

Origin

GFP

lacI

Origin

cI


Amit meshulam bioinformatics seminar technion spring 06

aTc

lacI

GFP

lacI

Origin

cI


Amit meshulam bioinformatics seminar technion spring 06

aTc

lacI

GFP

lacI

Origin

cI


Amit meshulam bioinformatics seminar technion spring 06

aTc

lacI

GFP

lacI

Origin


Amit meshulam bioinformatics seminar technion spring 06

aTc

lacI

GFP

lacI

Origin


Scenario demonstration1

Scenario demonstration

Input:

IPTG –

aTc –


Amit meshulam bioinformatics seminar technion spring 06

Pt

lacI

Pl

Lambda cI

Pt

tetR

PcI

GFP

tetR

tetR

Origin


Amit meshulam bioinformatics seminar technion spring 06

Pt

lacI

Pl

Lambda cI

Pt

tetR

PcI

GFP

tetR

lacI-

lacI+

tetR

Origin


Amit meshulam bioinformatics seminar technion spring 06

Pt

lacI

Pl

Lambda cI

Pt

tetR

PcI

GFP

tetR

lacI-

lacI+

Pl

Pl

Lambda cI

Lambda cI

Pt

Pt

tetR

tetR

PcI

PcI

GFP

GFP

From the origin gene

lacI

lacI

Origin

tetR

Origin


Amit meshulam bioinformatics seminar technion spring 06

Pt

lacI

Pl

Lambda cI

Pt

tetR

PcI

GFP

tetR

lacI-

lacI+

Pl

Pl

Lambda cI

Lambda cI

Pt

Pt

tetR

tetR

PcI

PcI

GFP

GFP

From the origin gene

lacI

lacI

Origin

tetR

Origin


Amit meshulam bioinformatics seminar technion spring 06

Pl

Pl

Lambda cI

Lambda cI

Pt

Pt

tetR

tetR

PcI

PcI

GFP

GFP

cI

GFP

cI


Amit meshulam bioinformatics seminar technion spring 06

Graphical representation

LacI+


Amit meshulam bioinformatics seminar technion spring 06

tetR

tetR

Origin

lacI

GFP

Origin

lacI

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

Origin

lacI

GFP

Origin

lacI

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

Origin

GFP

Origin

lacI

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

Origin

GFP

Origin

lacI

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

Origin

GFP

Origin

lacI


Amit meshulam bioinformatics seminar technion spring 06

tetR

Origin

GFP

Origin

lacI


Graphical representation

Graphical representation

LacI-


Amit meshulam bioinformatics seminar technion spring 06

tetR

tetR

lacI

GFP

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

lacI

GFP

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

GFP

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

GFP

cI


Amit meshulam bioinformatics seminar technion spring 06

tetR

cI


Facs analysis

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 analysis1

FACS analysis


Facs analysis2

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

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

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

Dependence of phenotypic behavior on network connectivity

Is connectivity of a network uniquely determine its behavior?

Negative!


Dependence of phenotypic behavior on network connectivity1

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 connectivity2

Dependence of phenotypic behavior on network connectivity


Dependence of network connectivity on phenotypic behavior

Dependence of network connectivity on phenotypic behavior

Is logical function uniquely determine its connectivity of network?

Negative!


Dependence of phenotypic behavior on network connectivity3

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 connectivity4

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 IF

NAND

NOR

NOR


Logical behavior of selected networks

NOR

Logical Behavior of selected networks

NOT IF

NOR


Combinatorial synthesis of genetic networks5

Combinatorial Synthesis ofGenetic 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

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

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.


Summary1

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.


Summary2

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.


Summary3

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.


Summary4

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 networks6

Combinatorial Synthesis ofGenetic 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

Comments

  • The article relates only to very specific networks.

  • There are no decisive conclusions.

  • No suggestions for generic approach.


Amit meshulam bioinformatics seminar technion spring 06

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