1. not a keynote, but a footnote on molecular biology and computation for Rocky 1 The Biology of Information Walter Fontana (SFI) walter@santafe.edu www.santafe.edu/~walter

3. 1. What can computation do for biology?

4. The computer as…

5. …theater: simulation, modeling

6. The computer as… …theater: simulation, modeling …library: organization of data

7. The computer as… …theater: simulation, modeling …library: organization of data …instrument: component of experiment

8. The computer as… …theater: simulation, modeling …library: organization of data …instrument: component of experiment …mathematical structure: formalism, concept

9. 1. What can computation do for biology?

10. Nothing.

11. 1. What can computation do for biology? A lot.

12. 1. What can computation do for biology? 2. What can biology do for computation?

13. …but this business is not well understood on both sides… molecular biology and computer science are in the same conceptual business

14. molecular biology and computer science are in the same conceptual business at the very minimum, both are about structure-behavior relations, i.e. configuring systems to engender specific behaviors (both are “programming” disciplines )

15. a self-printing program in C

16. now imagine these expressions… … decaying … moving around … combining into imprecise meanings … acting in parallel & asynchronously

17. a self-printing program now imagine these expressions… … decaying … moving around … combining into imprecise meanings … acting in parallel & asynchronously

18. molecular components… …turn over (from minutes to days) …are stochastic (wrt reliability, number, recognition) …move around (passively or actively) in a structured medium …communicate through physical contact …control each other’s state and production …are often multipurpose …need (lots of) energy for communication …operate concurrently

19. turn-over of components: persistence of identity memory of state stochasticity (in number and recognition): error-correction massive concurrency: emergence of determinism coordination & conflicts communication by contact: energy transport control of space …which entails a suite of issues, such as:

20. plasticity reconfigurability compressibility evolvability (neutrality, modularity) autonomy self robustness biological architectures emphasize systemic capacities, e.g. all these features are desirable but absent in present day computer architectures

21. + in biological systems, there is no “software running on something” ! IS NOT

22. in (theoretical) computer science… …physical hardware is distinct from software. (in CS, “machine” is a software notion) in biology… …physical hardware is software

23. dynamics stochasticity effective potentials combinatorial trajectories & path-dependency discrete events & concurrency object syntax and action generative interactions physics logicdigital analog

24. A few vignettes where the gap between computation and molecular biology is widest

25. enzyme kinetics 101 Who is the “signal”??

26. phosphorylation chain

28. multiple phosphorylation in proteins (phosphobase*) * A. Kreegipuu, N. Blom, S. Brunak. Nucleic Acids Research (1998/1999) W.Fontana & D.Krakauer (in progress)

29. phosphorylation chain and hypersensitivity

30. generalized signaling cascades

31. shifting the threshold by positioning P-chains of different width at various depths in a cascade

32. pulse filter

33. multiple phosphorylation as pulse filter W.Fontana & D.Krakauer (in progress)

34. multiple phosphorylation as pulse filter W.Fontana & D.Krakauer (in preparation)

35. memory and “checkpoints”

36. phosphorylation chain

37. phosphorylation chain with positive feedback

38. phosphorylation chain with symmetric feedback

40. |relative average diff of end states| n/signal large J: Bose-Einstein small J: Curie-Weiss S.Krishnamurty, E.Smith, D.Krakauer, W.Fontana Phys.Rev.Lett., submitted stochastic treatment of a P-chain with symmetric feedback second order phase-transition

41. stochastic master equation introduce operator algebra familiar from many-body physics obtain equivalent equation, now approachable by techniques from many-body physics effective potentials idea by M.Sasai & P.Wolynes:

42. Sasai & Wolynes: “Stochastic gene expression as a many-body problem”, PNAS, 100, 2374–2379 (2003). the landscape concept made formally precise by techniques from statistical mechanics “programming” becomes sculpting an appropriate landscape. But how? (cf. neural networks, spin glasses…) the landscape metaphor: from energy landscapes in proteins to epigenetic landscapes a la Waddington

43. reconfigurable molecular networks, plasticity

44. Milan N Stojanovic, Darko Stefanovic. Nature Biotechnology, 21, 1069 - 1074 (2003) allosteric RNA gates

45. Why do we need the formalisms of computation and logic? a pragmatic answer: more tools get us to more places. a deeper answer: because we need a theory of (molecular) objects. Why? Because the pressing (and recalcitrant) question for biology is not only to describe the behavior of a particular system, but to understand that system in the context of the possible, i.e. of what is evolutionarily accessible to it. Stated differently: we must eventually be able to reason about novelty. We never can do so within the confines of dynamical systems, because dynamical systems do not represent the objects they are made of. (Remember chemistry.)

46. we need an abstraction of chemistry in which molecules are interacting computational agents the grand challenge: describe a system with an expression that is at the same time a program to “run” that system AND a formula to reason about it abstractly.

47. A brief coda where the gap between computation and molecular biology is closing (at the formal language end)

48. inputoutput function no interaction with the “environment” Old notion of computation semantics: input-output relation

49. process semantics: potential sequences of interaction events interaction with the “environment” New notion of computation

50. function closed system process open system computation : analogy in physics: equilibrium normal form organizationmain concern:

51. Theory of concurrency, Process algebra Robin Milner, Communicating and Mobile Systems: the  -calculus, Cambridge (1999)

52. The  -calculus (Milner, Walker and Parrow 1989) a program specifies a network of interacting processes processes are defined by their potential communication activities communication occurs on complementary channels, identified by names message content: channel name

53. Aviv Regev, Ehud Shapiro, Corrado Priami, and others: application of concurrency / process algebras to molecular signal transduction A.Regev & E.Shapiro, Nature, 419, 343 (2000), Concepts

54. concurrency theory, what for? tool for agent-based simulation based on a theory of the agents tool for agent-based simulation at worst: at its most hopeful:

55. a lingua franca?