CG020 Genomika Bi7201 Základy genomiky 10. Systémová biologie 10. Systems biology Kamil R ůžička

1. CG020 Genomika Bi7201 Základy genomiky 10. Systémová biologie 10. Systems biology Kamil Růžička Funkční genomika a proteomika rostlin, Mendelovo centrum genomiky a proteomiky rostlin, Středoevropský technologický institut (CEITEC), Masarykova univerzita, Brno kamil.ruzicka@ceitec.muni.cz, www.ceitec.muni.cz

2. Přehled • What is systems biology • System theory • Omics • Reductionism vs. holism • Networks • Modular concept • Regulation of gene expression – example task for systems biology • Gene regulation X->Y • Transcriptional network of E. coli • Negative autoregulatory networks • Robustness of negative autoragulatory networks • (Positive autoregulatory networks)

3. What is systems biology • fashionable catchword? • a real new (philosophical) concept? • new discipline in biology? • just biology?

4. What is systems biology • fashionable catchword? • a real new (philosophical) concept? • new discipline in biology? • just biology? http://www.ceitec.eu/programs/genomics-and-proteomics-of-plant-systems/

5. Systems theory • The behavior of a system depends on: • Properties of the components of the system • The interactions between the components

6. Systems theory • The behavior of a system depends on: • Properties of the components of the system • The interactions between the components Forget about reductionism, think holistically. ὅλος [hol'-os] – greek. all, the whole, entire, complete

7. Systems biology meeting of old and new • Systems theory and theoretical biology are old • Experimental and computational possibilities are new

8. Ludwig von Bertalanffy (1901-1972)

9. Omics-revolution shifts paradigm to large systems - Integrative bioinformatics - (Network) modeling

10. Two roots of systems biology Palsson 2007

11. Associated disciplines • Genomics • Epigenomics • Transcriptomics • Translatomics / Proteomics • Interactomics • Metabolomics • Fluxomics • NeuroElectroDynamics • Phenomics • Biomics

12. Associated disciplines • Genomics • Epigenomics • Transcriptomics • Translatomics / Proteomics • Interactomics • Metabolomics • Fluxomics • NeuroElectroDynamics • Phenomics • Biomics JozefMravec’s term: multidimensional biology

13. How I understand systems biology • Genetics – you have one or few RNA processing genes where you show their importance in protoxylem development • Functional genomics– you find in e.g protoxylem expression profiles numerous RNA processing genes and demonstrate which are important for protoxylem developments • Systems biology– based on obtained large scale data you propose model how genes (and/or other components) collectively regulate protoxylem development

14. How I understand systems biology • Good biology – you explain why just some genes regulate protoxylem development (sorry for aphorisms)

15. Reconstructed genome-scale networks ArabidopsisInteractomeMappingConsortium (2011), Science

16. Reconstructed genome-scale networks ArabidopsisInteractomeMappingConsortium (2011), Science

17. Complexity of cellular networks in E. coli

18. Sometimes the things are different than we just think

19. Reconstruction of networks from -omics for systems analysis • Gene expression networks: based on transcriptional profiling and clustering of genes • Protein-protein interaction networks(Y2H, TAP etc). • Metabolic networks: network of interacting metabolites through biochemical reactions.

20. Reconstruction of networks from -omics for systems analysis • Gene expression networks: based on transcriptional profiling and clustering of genes • Protein-protein interaction networks(Y2H, TAP etc). • Metabolic networks: network of interacting metabolites through biochemical reactions.

21. How to simplify.Modularity concept. Lets e.g. assume that transcription and translation is one module.

22. E. coli Generation time 20 min

23. Description of gene regulation

24. Description of gene regulation [units [time-1]

25. Description of gene regulation [units [time-1]

26. Description of gene regulation [units [time-1] cells grow protein is degraded +

27. Description of gene regulation

28. 1. Steady state – ustálený stav Y Yst t

30. 2. Production of Y stops: (log => ln [.CZ])

31. 2. Production of Y stops: Large 𝛼 → rapid degradation (log => ln [.CZ])

32. Stable proteins(most of E. coli proteins) τ – cell generation

33. Stable proteins τ – cell generation Response time is one generation.

34. 3. Production of Y starts from zero ß t

35. 3. Production of Y starts from zero

36. 3. Production of Y starts from zero (magic) Y grows almost linearly initially

37. 3. Production of Y starts from zero Response time: The same response time as in case 2. Response time does not depend on production rate!

38. 3. Production of Y starts from zero Response time: Not many degradation mechanisms in E. coli (energy consuming). Perhaps in plants?

39. Networks node (CZ: uzel) thread (CZ: hrana)

40. Transcriptional network of E. coli 420 nodes, 520 edges How may self-edges? (CZ: samohrana?)

41. Likelihood of the self-edge

42. Autoregulation is a network motif 420 nodes, 520 edges. 40 self-edges!

43. Autoregulation is a network motif negative regulation positive regulation E. coli:40 autoregulatory loops: 36 negative, 4 positive

44. Negative autoregulatory loop is best described by Hill’s function ß(Y) Y

45. Negative autoregulatory loopsHill’s function ßmax ß(Y) n ß1/2 K Y

46. Negative autoregulatory loopsHill’s function maximum production rate ßmax ß(Y) n ß1/2 K Y steepness (Hill’s function) concentration of Y needed for 50 % repression

47. Negative autoregulatory loopsHill’s function ßmax ß(Y) maximum (initial) production rate n ß1/2 K Y

48. Negative autoregulatory loopsß synthesis rate – stochastic noise (stochastický ruch) ß(Y) Y ß may vary by 10 - 30 % (other parameters stable)

49. Negative autoregulatory loopsHill’s coeficcient ß(Y) • varies between 1 – 4, the higher the steeper • important factor: multimerization n ß1/2 K Y

50. Negative autoregulatory loopsK – repression coefficient • depends on chemical bonds between Y and its binding sites • a point mutation can increase K ~10 times ß(Y) n ß1/2 Y Y ATG NNNNN K Y concentration of Y needed for 50 % repression