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Engineered Gene Circuits Jeff Hasty

Engineered Gene Circuits Jeff Hasty. How do we predict cellular behavior from the genome? Sequence data gives us the components, now how do we understand the full system?

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Engineered Gene Circuits Jeff Hasty

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  1. Engineered Gene Circuits Jeff Hasty

  2. 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?

  3. Gene Regulation

  4. 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 B A C D E

  5. John Tyson’s Analogy “Using gene and protein network wiring diagrams to try to deduce cellular behavior is akin to using a VCR circuit diagram to try to deduce how to program it.” Mathematical models are needed to translate gene-protein wiring diagrams into “manuals” explaining cellular processes. But how do we construct reliable and useful mesoscopic models?

  6. 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

  7. Engineered Toggle Switch Model - design criteria: Construction/experiments: Gardner, Cantor & Collins, Nature403:339 (2001)

  8. The Repressilator Elowitz and Leibler, Nature403:335 (2001)

  9. 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 PNAS97:2075, 2000)

  10. Biochemical Reactions

  11. Rate Eqs For cI Monomers and GFP Reporter Model predictions as the temperature is varied?

  12. Model Prediction: Multistability

  13. Experimental Protocol

  14. Bistability Results Prediction Observation

  15. 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

  16. Model Versus Experiment

  17. Coefficient of Variation

  18. Genetic Relaxation Oscillator Hasty et al, Chaos (2001)

  19. Relaxation Oscillator Analysis Design network so that y is a slow variable:

  20. Drive Oscillator With Cell Division Cycle Identify known oscillating gene product and its target promoter SWI4 forms a complex and activates the HO promoter

  21. Resonant Dynamics

  22. 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)

  23. Collaborators: 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

  24. The Human Genome Project Secret of Life Solved! • Why is this not true? • Network dynamics not yet understood Cells fully understood! Molecular biology finished!

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