engineering synthetic oscillatory gene networks at the population level
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Engineering Synthetic Oscillatory Gene Networks at the Population Level Duke University Genetically Engineered Machines 2006 Sagar Indurkhya, Nicholas Tang, Austen Heinz, and Lingchong You Durham, North Carolina 27708, U.S.A. Characterization. Objectives and Approach - PowerPoint PPT PresentationTRANSCRIPT
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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.
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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.