amit meshulam bioinformatics seminar technion, spring 06

Combinatorial Synthesis ofGenetic Networks

Calin C. Guet, Michael B. Elowitz, Weihong Hsing,Stanislas Leibler

Amit MeshulamBioinformatics Seminar

Technion, Spring 06


• 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..

• The goal is to predict the cell behaviorpredict 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

Promoter Exons

DNA

Enhancer

Regulator Protein

Example - Inter biological elements interactions (Ecoli)

Example of biological network

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:- IPTGIPTG – The inducer that binds to the LacI protein and

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

prevent the binding to the target DNA.


• 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.


• 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

Pi lacI Pj Lambda cI Pk tetR


• 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

Pi lacI Pj Lambda cI Pk tetR PcI GFP


• 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

Combinatory library construction

• Using modular genetic cloning strategy generating combinatorial libraries of logical circuits.

• Construction of the library proceeded in two stepsStep 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

technique PCR

5’

5’

5’

5’

3’

3’

3’

3’

Step 2: In-order fragment fusion

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

Pi lacI Pj Lambda cI Pk tetR PcI GFP


• How to ensure the in-order fragments How to ensure the in-order fragments fusion?fusion?

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

• Post-restriction:

ATTGCCATCGGNNNNNCCGTCGCAAT

TAACGGTAGCCNNNNNGGCAGCGTTA

TAACGGTAGCCNNNN

ATTGCCATCGGN

NGGCAGCGTTA

NNNNCCGTCGCAAT


• 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 APaNGGCAGCGTT

NNNNCCGTCGCAAT

X

Pb Gene B

Pa Gene A Pb Gene B


• 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

• 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: aTcIPTG

++

-+

+-

--

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+

tetROrigin

aTc

Pt lacI Pl Lambda cI Pt tetR PcI GFP

tetR

aTc

tetROrigin

aTc

lacI



tetR

aTc

tetROrigin

aTc

lacI


tetROrigin

aTc


tetR

aTc

tetROrigin

aTc

cIGFP


Graphical representation

tetR

lacI

lacIOrigin

cI

GFP

aTc

tetROrigin

lacI

lacIOrigin

cI

GFP

aTc

lacI

lacIOrigin

GFP

aTc

Scenario demonstration

Input:

IPTG–

aTc–

tetR


tetROrigi

n

tetR


lacI - lacI+

tetROrigi

n

tetR


lacI - lacI+

lacIOrigi

n

From the origin gene

Pl Lambda cI Pt tetR PcI GFP Pl Lambda cI Pt tetR PcI GFP

lacI

tetROrigi

n

GFP

Pl Lambda cI Pt tetR PcI GFP

cI

Pl Lambda cI Pt tetR PcI GFP

cI


LacI+

tetR

lacI

OriginlacI

cI

GFP

tetROrigin

lacI

OriginlacI

cI

GFP

tetROrigin

OriginlacI

cI

GFP

tetROrigin

OriginlacI

GFP

tetROrigin


LacI-

tetR

lacI

cI

GFP

tetR

lacI

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

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?


• For example – the following tow networks have the same connectivity but different logical behavior.

Dependence of network connectivity on phenotypic behavior

Is logical function uniquely determine its connectivity of network?


Networks can differ by their connectivity but have qualitatively the same logical function.

For example:

NOR


A single change of the promoter can completely modify the behavior of the logical circuit.

For example:NOT IF NAND

NORNOR

Logical Behavior of selected networks

IPTG

aTc

-

-

+

-

-

+

+

+

NOR

NOT IF

NAND

NOR

NOT IFNOR

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.

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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