In silico discovery of principles in multiscale Systems Biology

1. Manchester Centre for Integrative Systems Biology Doctoral Training Centre for Systems Biology from Molecules to Life In silico discovery of principles in multiscale Systems Biology Hans V. Westerhoff and friends Netherlands Institute for Systems Biology, Amsterdam

2. In silico discovery of principles in multiscale Systems Biology • The paradigm of the replica model • Discovery through replica modelling of reality: • Time invariance and distributed control • Robust biology • Irreducible complexity • Drug target discovery • Hierarchies in scales: gene expression and time • We have a problem • MultiscaleITFoM as a solution • Out of my comfort zone

3. The enzymes are like elementary particles for biology! • X=X(time, X0, e1, e2,.., en, enzyme parameters, [S]) Constituent equation: ∙ chemical ─ reaction

4. The paradigm of the replica model • Model reality using multiscaling that does not loose essential complexity • Genes/enzymes as elementary particles • Describe them with rate equations (v(X)) • Describe metabolites with node equations (dX/dt) = N.v) • Integrate • Repeat at higher scales in terms of modules, keeping relationships with fine-grained levels

6. Silicon / virtual biochemical organisms

7. In silico discovery of principles in multiscale Systems Biology • The paradigm of the replica model • Discovery through replica modelling of reality: • Time invariance and distributed control • Irreducible complexity • Robust biology • Drug target discovery • Hierarchies in scales: gene expression and time • We have a problem • MultiscaleITFoM as a solution • Out of my comfort zone

8. If the model is a replica, it is as complex as the real system, hence offers no advantages for understanding Replica models can be used for computational investigations of reality They greatly facilitate discovery of Principles that govern reality

10. HendrikAntoon Lorentz • 1900: Maxwell equations are • invariant under the Lorentz transformation • Lorentz contraction

11. Ourtransformation Allprocesses 60 timesfaster Seconds instead of minutes as time unit Constituent equation: Thereshouldbe no effect ∙ chemical ─ reaction

12. Law/principle of Systems Biology C=Control of concentration by enzyme lnJ log of concentration SS Westerhoff (2008) J Theor Biol 252, 555 - 567 Steady state or maximum: logarithm of time

13. Silicon / virtual biochemical organisms\validated in silico

14. The principle we discovered Growth factor E EP For the maximum level of EP the phosphatases are equally important as the kinases FFP GGP Transcription of ‘growth’genes

15. In silico discovery of principles in multiscale Systems Biology • The paradigm of the replica model • Discovery through replica modelling of reality: • Time invariance and distributed control • Irreducible complexity • Robust biology • Drug target discovery • Hierarchies in scales: gene expression and time • We have a problem • MultiscaleITFoM as a solution • Out of my comfort zone

16. Simplicity: Control essentially in one component(the key gene/enzyme catalyzing the first irreversible step)Irreducible complexity:Control is distributed And not even uniformly Which is it?

17. 0.03 0.06 -0.43 0.00 -0.18 0.21 0.01 0.43 1.47 -1.47 -1.12 0.44 -0.44 MAP kinase signaling: which are the fragile steps? Healthy tissue Calculations based on Schöberl model At JWS/SiC Hornberg et al. Oncogene

18. In silico discovery of principles in multiscale Systems Biology • The paradigm of the replica model • Discovery through replica modelling of reality: • Time invariance and distributed control • Irreducible complexity • Robust biology • Drug target discovery • Hierarchies in scales: gene expression and time • We have a problem • MultiscaleITFoM as a solution • Out of my comfort zone

19. To discover & certify network principles of robustness (and disease) We need a definition of robustness

20. Definition of robustness The percentage by which one can interfere with a molecular process without reducing system function by more than 1 %

21. Principle 1 Networking enhances robustness

22. Process in isolation Robustness is 1 for processes in isolation Function Enzyme activity

23. robustness of isolated processes =1 Is the robustness in networks larger?

24. Silicon / virtual biochemical organisms

25. Robustness of vital flux of Trypanosomes vis-à-vis perturbation ofvarious glycolytic steps Question: Is robustness higher (than 1) in networks of living cells? Answer: Yes, most robustnesses in networks in living organisms are large; average is 468 here

26. Principle 2??? Trade-off???: Does making the system more robust vis-à-vis one perturbation make it equally less robust for a different perturbation???

27. Precise trade-off for robustness? No, robustness is not conserved No precise trade-off for robustness

28. Principle 2??? Trade-off???: making the system more robust vis-à-vis one perturbation makes it less robust for a different perturbation???

29. No principle then?No trade-off? Yes, there is one!

30. Sum over all inverse robustnesses = 1= conserved

31. In silico discovery of principles in multiscale Systems Biology • The paradigm of the replica model • Discovery through replica modelling of reality: • Time invariance and distributed control • Irreducible complexity • Robust biology • Drug target discovery • Hierarchies in scales: gene expression and time • We have a problem • MultiscaleITFoM as a solution • Out of my comfort zone

32. Trypanosomiasis

33. Silicon / virtual biochemical organisms

34. The most fragile step is….. ? ?

35. Differential network-based drug design Target where the difference between parasite and host is the largest

36. Red blood cell Holzhütter et al. Trypanosome in the host T. brucei….. us et al.

37. 0.00 0.68 0.02 0.03 0.00 0.001 0.02 0.005 -0.01 0.05 0.00 0.01 0.00 0.06 0.00 0.07 0.00 0 0.94 0.01 0.03 0.001 Differential fragility analysis TRYP and ERY Fragility of ATP synthesis flux GOODTARGET TRYPANOSOME BADTARGET ERYTHROCYTE BADTARGET FAIRTARGET (Bakker, Holzhütter, Snoep, Westerhoff)

38. Haanstra

40. Fragilities of PGK mRNA and protein versus perturbations in .. Fragility of for →: Targeting the networks: multiple targets at the same time in hierarchical networks!

41. The multiscaleproblemandtranscriptionactivation Time: How to bridge the various time scales? Molecular <1 s versus Cellular >1 h The multidimensionproblem: How toenableregulationby 20 information flowsratherthanby 1?

42. The clock model formammalian transcriptionactivation B A B A C A B C A D A B D C A B D C B C D D D C

43. Slow macroscopicdynamicscausedby, rapid, molecularprocesses! Metivier, R. et al.Estrogen receptor-alpha directs ordered, cyclical, and combinatorial recruitment of cofactors on a natural target promoter. Cell115, 751-763 (2003). Karpova, T. S. et al. Concurrent fast and slow cycling of a transcriptional activator at an endogenous promoter. Science (New York, N.Y319, 466-469 (2008). Saramaki, A. et al. Cyclical chromatin looping and transcription factor association on the regulatory regions of the p21 (CDKN1A) gene in response to 1alpha,25-dihydroxyvitamin D3. J BiolChem284, 8073-8082, doi:M808090200 [pii] Note! Transcriptionsynchrony in population of cells!

44. In silico discovery of principles in multiscale Systems Biology • The paradigm of the replica model • Discovery through replica modelling of reality: • Time invariance and distributed control • Irreducible complexity • Robust biology • Drug target discovery • Hierarchies in scales: gene expression and time • We have a problem • MultiscaleITFoM as a solution • Out of my comfort zone

45. Why?Well, we have ‘a’ problem Definitivecures are lackingfor most diseases The health care budget willcripple the economy The life sciences are tremendouslysuccessful but ….. not in empoweringmedicine ………….

46. Increased spending has not improved cancer mortality

47. Global prevalence of diabetes and impaired glucose tolerance (IGT) in 2010 and 2030 Boyle, 2011

48. genomics transcriptomics proteomics Yet… We can measure almost everything now metabolomics structural biology biochemistry biophysics biology physiology

49. >1 trillion €/year spent on biomedical research: Tower of Babel? health disease Brueghel

50. In silico discovery of principles in multiscale Systems Biology • The paradigm of the replica model • Discovery through replica modelling of reality: • Time invariance and distributed control • Irreducible complexity • Robust biology • Drug target discovery • Hierarchies in scales: gene expression and time • We have a problem • MultiscaleITFoM as a solution • Out of my comfort zone