Engineered Gene Circuits Jeff Hasty
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Engineered Gene Circuits
Jeff Hasty
OR
cI857
gfp
RBS
RBST1T2
ampR
ColE1
T1T2
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How do we predict cellular behavior from the genome? Sequence data gives us the components, now how do we understand the full system?
How can we control or monitor cellular behavior? Diseases, pathogenic invasions involve alterations of natural dynamics - can we reestablish normal function?
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Gene Regulation
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Gene regulatory networks• Proteins affect rates of production of other
proteins (or themselves)
• This allows formations of networks of interacting genes/proteins– Sets of genes whose expression levels are
interdependent
A
B
C
D E
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““Using gene and protein network wiring Using gene and protein network wiring diagrams to try to deduce cellular behavior diagrams to try to deduce cellular behavior is akin to using a VCR circuit diagram to try is akin to using a VCR circuit diagram to try to deduce how to program it.”to deduce how to program it.”
Mathematical models are needed to Mathematical models are needed to translate gene-protein wiring diagrams translate gene-protein wiring diagrams into “manuals” explaining cellular into “manuals” explaining cellular processes.processes.
But how do we construct reliable and useful But how do we construct reliable and useful mesoscopic models?mesoscopic models?
John Tyson’s Analogy
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Engineered Gene Circuits
Faithful modeling of large-scale networks is difficult…
Alternative: Design and build simpler networks
Decouple complexity
Use model to design experiments
Systematic comparison of model and experiment
“Forward Engineering” of useful circuits
Design networks to perform tasks
Couple to host - control or monitor cellular function
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Engineered Toggle Switch
Gardner, Cantor & Collins, Nature 403:339 (2001)
Gene A onGene B off Reporter
G FPRepressor A
Gene A offGene B on Reporter
Repressor B
“On”
“Off”
Model - design criteria:
Construction/experiments:
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The RepressilatorG FPA
B C
Gene A
Gene B Gene C
Elowitz and Leibler, Nature 403:335 (2001)
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A Detailed Example: Single-Gene Autoregulatory Module
Well-characterized: Kinetic parms known
Tunable control: CI857 denatures with temp
Build network with off-the-shelf molecular biology
Theoretical predictions: Bistablity and hysteresis
(Hasty et al PNAS 97:2075, 2000)
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Biochemical Reactions
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Rate Eqs For cI Monomers and GFP Reporter
Model predictions as the temperature is varied?
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Model Prediction: Multistability
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Experimental Protocol
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Bistability Results
Prediction
Observation
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Model the Fluctuations
- OK when fluctuations dominated by production and degradation
- Distributions numerically check with Monte Carlo “gold standard”
- Still working on systematic demonstration of validity
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Model Versus Experiment
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Coefficient of Variation
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Genetic Relaxation Oscillator
O 2R O 2R
Prom oter P R M
cI
O 3R
Prom oter P R M
Plasm id 2P lasm id 1
RcsA
O 3R
Hasty et al, Chaos (2001)
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Relaxation Oscillator Analysis
Design network so that y is a slow variable:
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Drive Oscillator With Cell Division Cycle
Identify known oscillating gene product and its target promoter
SWI4 forms a complex and activates the HO promoter
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Resonant Dynamics
Drive Period (Minutes)
Regions of synchronization
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Summary
• Use of biochemical kinetics to describe gene regulation (in bacteria)
• Models can be used to develop “tailor-made” circuits
• Gene circuits lead naturally to problems relevant to nonlinear dynamics, statistical physics and engineering
• Noise from small molecular numbers is a dominant source
• Genetic “states” accessed through fluctuations (noise-induced transitions between attractors)
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OR
cI857
gfp
RBS
RBST1T2
ampR
ColE1
T1T2
Milos Dolnik (Brandeis)
David McMillen (Boston University)
Vivi Rottschafer (Leiden)
Farren Isaacs (BU)
Charles Cantor (BU-UCSD)
Jim Collins (BU)
Funding: NSF, DARPA and the Fetzer Institute
Collaborators:
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The Human Genome Project
• Why is this not true?
• Network dynamics not yet understood