Engineering Synthetic Oscillatory Gene Networks at the Population LevelEngineering Synthetic Oscillatory Gene Networks at the Population LevelDuke University Genetically Engineered Machines 2006Duke University Genetically Engineered Machines 2006

Sagar Indurkhya, Nicholas Tang, Austen Heinz, and Lingchong You Sagar Indurkhya, Nicholas Tang, Austen Heinz, and Lingchong You

Durham, North Carolina 27708, U.S.A.Durham, North Carolina 27708, U.S.A.

Engineering Synthetic Oscillatory Gene Networks at the Population LevelEngineering Synthetic Oscillatory Gene Networks at the Population LevelDuke University Genetically Engineered Machines 2006Duke University Genetically Engineered Machines 2006

Sagar Indurkhya, Nicholas Tang, Austen Heinz, and Lingchong You Sagar Indurkhya, Nicholas Tang, Austen Heinz, and Lingchong You

Durham, North Carolina 27708, U.S.A.Durham, North Carolina 27708, U.S.A.

AbstractEngineered Genetic Machines can serve as effective gene delivery, drug production, and metabolic platforms, while shedding fundamental insight on natural biological systems. We have developed two novel gene circuits: a synthetic predator prey ecosystem and a multistage genetic oscillator using both new and novel strategies. Both circuits operate at the population level, synchronizing behavior through a biochemical process known as quorum sensing. To date they represent two of the most complex artificial biological systems ever attempted. Our work has given a detailed description of several natural processes while at the same time developed new biological parts and computational tools to further advance the rapidly developing field of synthetic biology.

Characterization

Acknowledgements

Duke University- Jingdong Tian - Faisal Reza

The North Carolina School of Science and Math- Myra Halpin- Bob Gotwals

Objectives and ApproachTo explore oscillating gene circuits at the population level for the purpose of developing methods and models for the engineering of more complex cellular behaviors

Optimizing mathematical models

In vitro characterization

Computational characterization

Artificial oscillating populations demonstrate the poten-tial for larger and more complex synchronized genetic circuits, which allow for drug delivery and integrated regulation of neuronal, metabolic, and cardiac systems.

Computational ChemistryAnalysis:

Similar HOMO (highest occupied molecular orbital)/LUMO (lowest unoccupied molecular orbital) gap values indicate that degradation and hydrolysis of the molecule correspond to the various sites of excitability.

These states also appear to correspond to the previously identified routes to inactivation of AHL, such as hydrolysis of the lactone ring, hydrolysis of the amide bond, and racemization.

Using data from previous experimental characterization of degradation rates for the lux small molecule, we fit a power curve to approximate degradation rates for all four small molecules. These results provide predictive potential for degradation rates.

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Prey Cin Predator Luxmax,cin max, lux

m,cin Cin m,lux Lux

Cin LuxHSL Formation HSL Formation

Expression by Small Molecule Promotion

d Blip HSL d AmpR HSL=V =V

dt K + HSL dt K + HSL

Formation of HSL Molecule

d HSL d HSL= k cinR cinI = k luxR

dt dt

⎡ ⎤⎣ ⎦

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.

.

LVA Protein Deg. Protein Deg. Protein Deg.

LVA Protein Deg. Protein Deg. Protein Deg.

Protein Deg.

luxI

Degradation of Molecules

d GFP d cinI d luxR= k GFP = k cinI = k cinR

dt dt dtd RFP d luxI d luxR

= k RFP = k luxI = k luxRdt dt dt

d AmpR d H= k AmpR

dt

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lux cinHSL Deg. lux HSL Deg. cin

PreyPredatorProtein Deg. Predator Protein Deg. Prey

lac lac lac lac

lac lac

SL d HSL= k HSL = k HSL

dt dt

d Blipd Blip= k Blip = k Blip

dt dtExpression by Gene Repression

d RFP d luxRH K H K= =

dt K + lacI dt K + tet

⎡ ⎤⎣ ⎦ ⎡ ⎤⎣ ⎦

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lac lac

lac

lac lac lac lac lac lac

lac lac lac

Predator lac lac

lac

d cinR H K=

R dt K + lacI

d GFP d luxI d cinIH K H K H K= = =

dt K + lacI dt K + lacI dt K + tetR

d Blip H K=

dt K + lacI

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Rhl Cin Luxmax, rhl max,cin max,lux

m, rhl Rhl m,cin Cin m,lux Lux

RBS Rhl RBS Cimax, rhl max,cin

m, rhl Rhl

Expression by Small Molecule Promotion

d tetR HSL d lacI HSL d cI l HSL=V =V =V

dt K + HSL dt K + HSL dt K + HSL

d cinI HSL d luxI HSL=V =V

dt K + HSL dt

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n RBS Luxmax,lux

m,cin Cin m,lux Lux

Rhl Cin Luxmax, rhl max,cin max,lux

m, rhl Rhl m,cin Cin m,lux Lux

Rhlmax, rhl m

m, rhl Rhl

d rhlI HSL=V

K + HSL dt K + HSL

d cinR HSL d luxR HSL d cinR HSL=V =V =V

dt K + HSL dt K + HSL dt K + HSL

d CFP HSL d YFP=V =V

dt K + HSL dt

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Cin Luxax,cin max,lux

m,cin Cin m,lux Lux

tet m,tet lac m,lac cI l m,cI lmRNA mRNA mRNA

m,tet m,lac m,cI l

HSL d CFP HSL=V

K + HSL dt K + HSL

Expression by Gene Repression

H K H K H Kd rhlI d cinI d luxI= = =

dt K + tetR dt K + lacI dt K + cI l

Formation of HSL Mol

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Cin Lux RhlHSL Formation HSL Formation HSL Formation

Protein Formation RBS mRNA Protein Formation RBS mRNA Protein Formation

ecule

d HSL d HSL d HSL= k cinR cinI = k luxR luxI = k rhlR rhlI

dt dt dtd cinI d luxI d rhlI

= k cin cin = k lux lux = k rdt dt dt

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RBS mRNA

LVA.Protein Deg. Protein Deg. Protein Deg.

LVA.Protein Deg. Protein Deg. Protein Deg.

LVA.Protei

hl rhl

Degradation of Molecules

d RFP d cinI d luxR= k RFP = k cinI = k cinR

dt dt dtd CFP d luxI d luxR

= k CFP = k luxI = k luxRdt dt dt

d YFP= k

dt[ ] [ ] [ ] [ ] [ ]

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n Deg. Protein Deg. Protein Deg.

lux cin rhlHSL Deg. lux HSL Deg. cin HSL Deg. rhl

RBS RBS RBSProtein Deg. RBS Protein Deg. RBS

d rhlI d rhlRYFP = k rhlI = k rhlR

dt dtd HSL d HSL d HSL

= k HSL = k HSL = k HSLdt dt dt

d cinI d rhlI d luxI= k cinI = k rhlI = k

dt dt dt[ ]

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Protein Deg. RBS

mRNA mRNA mRNAProtein Deg. RBS Protein Deg. mRNA Protein Deg. mRNA

luxI

d cinI d rhlI d luxI= k cinI = k rhlI = k luxI

dt dt dt

Compression of digital logic with-in gene circuits using Ribosome Binding Site (RBS) Regulation

Characterization of small molec-ule cross talk is necessary to pr-ovide gene circ-uit designers with solid found-ations.

Mathematical ModelingX-VerterA Predator-Prey Ecosystem

Predator-Prey

X-Verter

Assembly AnalysisWe have written a software package, Biobricks Manager, which autom-ates the assembly process in a customized Integrated Development Environment (IDE).

Handles multiple projects simultaneously (checking for reusable bricks) Automatically synchronizes information with the online Standard Parts Registry Automatically re-computes and optimizes the assembly process at each stage. Perform sequence analysis and automate the error detection process.

ConclusionWe have found similarities between the pH degradation values of the different quorum sensing models through computational chemistry. We have developed and studied two different complex oscillating gene networks that both operate at the population level through quorum-sensing based synchronization.