1. Evolvability and Cross-Talk in Chemical Networks Chrisantha Fernando Jon Rowe Systems Biology Centre & School of Computer Science Birmingham University, UK ESIGNET Meeting September 2007 Chrisantha Fernando Jon Rowe Systems Biology Centre & School of Computer Science Birmingham University, UK ESIGNET Meeting September 2007

2. Aims Model evolution and function of cellular networks Understand the principles of evolvability in cellular networks Model cross-talk Model evolution and function of cellular networks Understand the principles of evolvability in cellular networks Model cross-talk

3. Simulated Evolution of Protein Networks Bray and Lay, 1994

4. Tp L T

6. Conclusions The ‘genetic description’ of proteins used was very unevolvable, i.e. brittle. Stochastic simulation did not allow ‘futile cycles’ to be modeled efficiently. These are essential for information transmission. We moved to a more abstract representation of chemical networks, inspired by work in Eindhoven. The ‘genetic description’ of proteins used was very unevolvable, i.e. brittle. Stochastic simulation did not allow ‘futile cycles’ to be modeled efficiently. These are essential for information transmission. We moved to a more abstract representation of chemical networks, inspired by work in Eindhoven.

7. Turing Complete Enzyme Computers To Appear in European Conference in Artificial Life 2007 Lisbon.

10. Conclusion Although there is now an easy way of programming serial programs in enzyme controlled systems…. Implementation in a physical system is not trivial!! Parallel implementations are possible. But how could we get evolvable chemical networks in the real world? Although there is now an easy way of programming serial programs in enzyme controlled systems…. Implementation in a physical system is not trivial!! Parallel implementations are possible. But how could we get evolvable chemical networks in the real world?

11. Chemical Evolution

12. How did metabolism evolve?

13. The Chemoton Metabolism Template Membrane

15. Molecular autocatalysts are necessary for heredity. Some have 2 o effects that are beneficial to the compartment. Some energy is required for this ‘memory’. Catalysis Autocatalysis

16. New autocatalysts arise and integrate into existing intermediary metabolism Not a reflexive autocatalytic set! Substrate

17. Multiplication: Yes Heredity: Yes Variability: Macro not micro

19. Is there a limit to complexity increase? Yes, in this simple model, the probability of stable autocatalyst formation decreases!

20. The metabolic equivalent of Szathmary’s SCM

21. Conclusions A limit to complexity is imposed if chemical variability properties cannot be shaped by second order selection Self-isolation of ‘faulty’ components (Tan, Revilla, Zauner, 2005) What is second-order selection? A limit to complexity is imposed if chemical variability properties cannot be shaped by second order selection Self-isolation of ‘faulty’ components (Tan, Revilla, Zauner, 2005) What is second-order selection? Real chemicals embody variability rules as (modular) structures. Make a chemical description language capable of representing chemical equivalence classes abstractly, that allows adaptive variability. Evolve the system at the compartment level to maximize information transmission.

22. A Second order selection is selection on the basis of offspring fitness

23. B It can act on variability properties

24. Evolvability shaped by second order selection? Produce a CE-calculus, capable of representing the crucial functional properties of small molecules that allow them to be structured by second order selection to promote evolvability, information transmission, and effective search. Use Keppa (Vincent Danos, Harvard) Produce a CE-calculus, capable of representing the crucial functional properties of small molecules that allow them to be structured by second order selection to promote evolvability, information transmission, and effective search. Use Keppa (Vincent Danos, Harvard)

25. European Collaborations Arising Eors Szathmary, ThalesNano (Budapest) & Guenter Von Kiedrowski (Bochum), FP7 Large scale application. Evolution of Formose cycle combinatorial libraries Find lipid precursor that reacts with formose cycle sugars via phase- transfer autocatalysis yielding sugar-lipid conjugates. Study the formose cycle using such a precursor Study these subsystems under high pressure

26. A New Kind of Cell Signaling using RNAi Protein structure to function map is very complex. A simpler and possibly more evolvable CSN could be made from RNA. John Mattick’s work shows the large amount of non-translated RNA in cells. We published a simulator capable of modeling complex populations of interacting RNA molecules with simple 2 o structures. Protein structure to function map is very complex. A simpler and possibly more evolvable CSN could be made from RNA. John Mattick’s work shows the large amount of non-translated RNA in cells. We published a simulator capable of modeling complex populations of interacting RNA molecules with simple 2 o structures.

27. Bad Cross-Talk = Side-Reactions

28. a b c d

30. Minimal Replicase was a Restriction Ribozyme David Bartel and Jack Szostak barking up wrong tree?

31. Conclusions The simulator used a simplified model of nucleic acid interactions to test hypotheses about how autocatalytic RNA could function in the absence of protein enzymes. Further work will increase the range of secondary structures, e.g. hairpins. The simulator used a simplified model of nucleic acid interactions to test hypotheses about how autocatalytic RNA could function in the absence of protein enzymes. Further work will increase the range of secondary structures, e.g. hairpins.

32. Bacteria that can learn Replicate this experiment Is learning epigenetically heritable? Are there any associated macro-nuclear gene changes? (L. Landweber)

33. Cross-talk does association In collaboration with molecular biologists, (Prof. Pete Lund, Dr. Lewis Bingle) and Anthony Liekens we have designed Hebbian learning circuits in plasmids carried by E. coli. v = w.u dw i /dt = u i v

39. Peter Dittrich, Thorsten Lenser & Christian Beck ‘Evolver’ uses “Biobrick” primitives. It is a Synthetic Biology Toolbox

40. What to expect?

41. Later….

42. Cell Signaling Network Implementation

43. Conclusions Nature paper in prep. Grant applications for synthesis in prep. Future medical applications. Introduces learning concepts to systems biology. Nature paper in prep. Grant applications for synthesis in prep. Future medical applications. Introduces learning concepts to systems biology.

44. Liquid State Machines in Bacteria?

45. Why so Little Lamarckian Inheritance? ECAL 2007

46. Publications so far… http://www.cs.bham.ac.uk/~ctf/

47. Expected Publications Nature. Hebbian Learning (in collaboration with Eindhoven and Jena). Evolution. Second-order selection for evolvability. Nature. Hebbian Learning (in collaboration with Eindhoven and Jena). Evolution. Second-order selection for evolvability.