2. Can we make biological systems predictable? Pamela Silver Dept of Systems Biology Harvard Medical School Director, Harvard University Graduate Program in Systems Biology http://silver.med.harvard.edu/

3. The value of models and design The value of models and design One example of building a system with predictable properties One example of building a system with predictable properties Training a new type of scientist - infrastructure needs Training a new type of scientist - infrastructure needs Overview of Talk

4. Models and more models……. c. 1985 c. 2005

5. – –We can make useful things   Redesign of a system can test our understanding of its components “What I cannot create I cannot understand.” Richard Feynman   Biology presents an array of engineering possibilities that have thus far been unexplored Why make a predictable biology?

6. Biology and engineering Design concepts Design concepts – Sensation – Signal processing & communication – Modularity – Easy duplication Bugs or Features? – Self-repair – Evolvability

7. Biological Modularity   Examples of modularity: Genes (promoters, ORFs, introns, enhancers) RNA (Translation, stability, export, localization) Proteins (Targeting, DNA binding, dimerization, degradation) Pathways (Signaling, metabolism)   Biological design can test the limits of modularity What does Nature have to offer?

8. Standardized Parts

9. Bacterial Devices The Repressilator Toggle Switch (Elowitz et al) (Collins et al)

10. Scientific Challenges Make functional components? Make functional components? Measure component function quantitatively? Measure component function quantitatively? Functional higher order networks? Functional higher order networks? Predict the behavior of higher order networks? Predict the behavior of higher order networks?

11. Building cellular memory in eukaryotes A small success story

12. Activation cascades:Repression cascades: LexA LacI Zif-HIV*, Zif-erbB2* ERG2, Gli1, YY1 Modular construction of Transcription Factors Modular construction of Transcription Factors

13. Synthetic activator activates a minimal promoter DIC Reporter (YFP) Activator (RFP) Activator:Reporter: + activator- activator ~20 fold activation

14. Kinetics of an activation device

15. Building Networks with Standardized Parts Components for complex devices Components for complex devices Test predictions about topology of eukaryotic networks Test predictions about topology of eukaryotic networks age 0 age 1age 2 vs.

16. Auto-feedback loop as memory A: B: Memory device Autofeedback loop

17. Requirements for Autofeedback Loop A: B: A makes B

18. Requirements for Autofeedback Loop A: B: A makes B B persists in the absence of A

19. A: B: A makes B B persists in the absence of A B shows bi-stability time = ∞ Requirements for Autofeedback Loop

20. A makes B A:B: - A+ A DIC Reporter (YFP) Activator (RFP)

21. B persists in the absence of A - A + A DIC Reporter (YFP) Activator (RFP) - A A:B:

22. allow to grow Cell-based memory: B persists in the absence of A

23. Quantitative Properties can Predict System Behavior Time derivative on dilution rate Time derivative based on dilution rate Production rate

24. Systems Design with Predictable Properties from Synthetic Parts Functional higher-order networks Functional higher-order networks Individual components predict higher-order network Individual components predict higher-order network +=

25. Bioenergy

26. Engineering microorganisms for energy production Conclusions from the JASON report: Boosting efficiency of fuel formation form microorganisms is THE major technological application of Synthetic Biology major technological application of Synthetic Biology Engineering fuel production from microbes is a SYSTEMS problem (Microbes are more tractable than plants……) (Microbes are more tractable than plants……) Successful engineering requires a basic understanding of the system to be engineered (multiple feedback loops, etc) to be engineered (multiple feedback loops, etc) Need to minimize the oxygen sensitivity of fuel-forming catalysts in biological systems (logical engineering of systems and proteins) biological systems (logical engineering of systems and proteins) Study Leader Mike Brenner; 6/23/06 Study Leader Mike Brenner; 6/23/06

27. Training Scientists for the Future

28. General systems approach

29. An emerging field * * We welcome students from biology, computer science, mathematics, chemistry, physics, engineering… * * We use interdisciplinary approaches to address important biological and medical questions * While most other Ph.D. Programs will teach you the state of the art in the field, this program expects students to help create it! of the art in the field, this program expects students to help create it!

30. Our challenges and goals Can we enable collaboration and synergy amongst our students? Can we teach the biologists mathematical modeling? Can we teach the modelers to answer biological problems? Applied Mathematics (1) Mathematics (1) Electrical Engineering (2) Computational Biology (2) Immunology (1) Medicine (1) Biology (3) Biochemistry (3) Mathematical Biology (1) Genetics (1) Computer Science (1) Microbiology (1) Distribution of Systems Biology graduate students

31. Defining a systems biology curriculum Systems biology is an emerging field, without a defined curriculum biochemistryglycolysis, oxidative phosphorylation, etc. molecular biotranscription, translation, etc. systems bio??? No unified principles yet, no coherent textbook What role does ‘omics’ play?

32. Something certain in all of this: numbers matter Human intuitions about the behavior of complex biological systems are frequently wrong Mathematics helps us get it right Quantitative observation leads to discovery

33. Dynamical systems & foundations (Gunawardena) SB200: mathematical models in systems biology ODE models eigenvectors, eigenvalues phase plane (multi)stability hysteresis oscillators Linked equilibria & biological networks (Fontana) equilibrium, thermodynamics binding, multiple substrates kinase/phosphatase cascades adaptation motifs & logic graph theory Stochastics (Paulsson) probability and statistics stochastic chemical reactions numerical simulation kinetics, sensitivity fluctuations noise

34. Essential computational tools numerical solutions to ODE models stochastic simulations matrix manipulations phase portraits (pplane) etc. symbolic and numerical calculations algebra analytical solutions to a range of DEs notebook files etc. Neither program is free for academic use. Possible free alternatives: Octave, R

35. The learning curve for biologists Quantitative thinking & simulation when intuition is lacking, e.g.: mRNA -- synthesis constant, degradation constant protein -- synthesis 1st order in mRNA, degradation 1 st order in protein k1k1 k2k2 k3k3 k4k4 Which rate constants determine the time at which the protein reaches steady state? Which determine the steady state concentration of protein?

36. The learning curve for biologists k1k1 k2k2 k3k3 k4k4

37. Mathematical tools k1k1 k2k2 k3k3 k4k4 Differential equations, parameter space, phase space, stability Linear algebra: matrix manipulations, basis, Jacobian, eigen analysis Probability & statistics x = Ax + b

38. The learning curve for modelers Biological reality vs. models -- all models are wrong to some degree Understanding physical principles of biology: Is it okay to assume that phosphorylation and dephosphorylation are irreversible processes? If so, when are they irreversible? Why? Is there any meaningful way to compare a 1 st order rate constant to a 2 nd order rate constant? Is it really okay to eliminate one of these constants because it’s ‘slow’?

39. The learning curve for modelers Understanding physical principles of biology: Is the model based on sound principles? Is the model robust? Biology doesn’t operate in narrow parameter regimes!

40. The learning curve for everybody Understanding when and how a complex system can be simplified into a useful model

41. Does an abstract diagram communicate the essence of what it depicts? Spider doing a handstand (Droodles, Roger Price [Pencil on napkin, ca 1953])

42. Thank You! Research funding from: NIH, DOD, Merck, HHMI, Keck Foundation Office of the Provost, Harvard University