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The Biology of Information: Exploring the Intersection of Molecular Biology and Computation

This talk delves into the fascinating relationship between molecular biology and computation, exploring how computers can be used for simulation, modeling, data organization, and experimentation in biology. It also highlights the parallels and conceptual similarities between these two fields and discusses the potential for collaboration and advancement.

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The Biology of Information: Exploring the Intersection of Molecular Biology and Computation

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Presentation Transcript

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

  2. 1. What can computation do for biology?

  3. The computer as…

  4. The computer as… …theater: simulation, modeling

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

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

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

  8. 1. What can computation do for biology?

  9. 1. What can computation do for biology? Nothing.

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

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

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

  13. 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)

  14. a self-printing program in C

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

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

  17. 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

  18. …which entails a suite of issues, such as: 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

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

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

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

  22. analog digital physics • dynamics • stochasticity • effective potentials • combinatorial trajectories & path-dependency • discrete events & concurrency • object syntax and action • generative interactions logic

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

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

  25. phosphorylation chain

  26. phosphorylation chain

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

  28. phosphorylation chain and hypersensitivity

  29. generalized signaling cascades

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

  31. pulse filter

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

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

  34. memory and “checkpoints”

  35. phosphorylation chain

  36. phosphorylation chain with positive feedback

  37. phosphorylation chain with symmetric feedback

  38. phosphorylation chain with symmetric feedback

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

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

  41. 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

  42. reconfigurable molecular networks, plasticity

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

  44. 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.)

  45. 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.

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

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

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

  49. computation: function process analogy in physics: closed system open system equilibrium normal form main concern: organization

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