1. 1 Lesson 12 Networks / Systems Biology

2. 2 Systems biology  Not only understanding components! 1.System structures: the network of gene interactions and the mechanisms that modulate intracellular and multicellular structures. 2.System dynamics. How a system behaves over time under various conditions.

3. 3 What is a network?

4. 4 Networks  Networks represent relations among different elements node edge

5. 5 Social networks  Nodes: individual people  Edges: social interactions

6. 6 Cellular molecular networks  Nodes: molecules  Edges: interactions

7. 7 Signal transduction pathway

8. 8 Transcriptional networks

9. 9 Protein-protein interaction (PPI) networks Yeast PPI network

10. 10 Defining network (graph) properties

11. 11 Directed and non directed networks Non directed network Directed network

12. 12 Degree of a node  The degree (connectivity) of a node is defined as the number of (direct) neighbors The degree of c-Jun is 12

13. 13 Random networks: most nodes have a similar degree

14. 14 Most networks in real life do not behave like random networks! Barabasi and Albert, Nature Reviews 2004 Scale free networks: characterized by a small number of hubs, yet most nodes have 1-2 links only. Hubs: highly connected (large degree) nodes

15. 15 Scale-free networks  World-Wide Web  Citation distribution  Cellular-molecular networks  Social networks

16. 16 “… here we analyse the sexual behaviour of … individuals to reveal the mathematical features of sexual- contact network. ” The result: the network is also scale-free. This implies that “ strategic targeting of safe-sex education campaigns to those individuals with a large number of partners may significantly reduce the propagation of sexually transmitted diseases. ”

17. 17 Robustness (and sensitivity) of scale-free networks What happens when you “ damage ” a node in the network? Most nodes: will affect very few other nodes Hubs: will have a serious effect

18. 18 Robustness (and sensitivity) of scale-free networks Lethal Non-lethal Slow-growth Unknown Knockout effect: Positive correlation between connectivity and lethality

19. 19 Error and attack tolerance of complex networks  Tolerance: “ even when as many as 5% of the nodes fail, the communication between the remaining nodes in the network is unaffected ”

20. 20 Attacking a scale-free network  Hackers will attack hubs (yahoo, google … )  Parasites will attack hubs (anti-apoptotic proteins)  Cancer: attacking the p53 transcription factor

21. 21 Mean path length  Length (distance) between 2 nodes is the number of edges along the shortest path between these nodes

22. 22 “ Small world ” : average mean path length in social network = 6! “ 6 degrees of separation in human relations ” My colleague ’ s friend ’ s uncle ’ s neighbor ’ s wife ’ s boss …

23. 23 “ Small world ” in biological networks  Most biological networks are ultra-small  For example: 3 to 4 reactions connect most metabolite pairs

24. 24 Network motifs

25. 25

26. 26  Sub-networks in the whole network, composed of 3-4 nodes  13 types of 3-node directional sub- networks:  199 type of 4-node networks Network motif

27. 27 Are real networks enriched for specific motifs?  Enrichment: Do we see motif 13 more than expected in a certain network  What is expected? Compare to a random graph with the same number of nodes and edges

28. 28 Transcription network of E. coli  Consistent enrichment for two motifs:

29. 29 Comparison with transcription factor network of S. cerevisiae  Again!

30. 30 Comparison with neuronal network of C. elegans  Again!!!

31. 31 Comparison with network of electronic circuits  Yet again …

32. 32 Compared to other biological and technological networks Technological World Wide Web Electronic circuit Biological Food web (predator -prey) Synapses in neurons Transcription

33. 33 Information processing Energy flow Information processing

34. 34 What is the functional meaning of these motifs?  The feed-forward loop was studied in- depth in E. coli

35. 35 The feed-forward loop  The feed-forward loop was studied in-depth in the E. coli transcription factor (TF) network  Two TFs (X,Y) which regulate one gene (Z)

36. 36 AND or OR gate  X and Y are transcription factors of Z  Two possibilities: X Y Z AND X Y Z OR Only one is necessary to operate Z Both are necessary to operate Z

37. 37 What is the functional meaning of these motifs?  Allow robustness to small fluctuations of outside signals X Y Z AND Slow turn-on, fast turn-off Activating signal (e.g. cAMP in the arabinose system)

38. 38 What is the functional meaning of these motifs?  Allow robustness to small fluctuations of outside signals X Y Z AND Slow turn-on: first activate X and then Y Fast turn-off: if X is shut off, then everything is off Activating signal (e.g. cAMP in the arabinose system)

39. 39 What is the functional meaning of these motifs?  The opposite in the OR system – robustness to shut-down X Y Z OR Fast turn-on: if X is active then Z is on Slow turn-off: both X & Y have to be shut down to turn Z off Activating signal (e.g. FlhDC in the flagella system of E.coli)

40. 40 Protein networks in diseases Ideker and Sharan, 2008. Genome Research 18:644-652

41. 41 Protein networks in cancer  High connectivity of up-regulated cancer genes

42. 42 Protein networks in other diseases  Low connectivity of most other disease- related genes  Cancer has a unique mode of action  Bias in our knowledge on cancer genes

43. 43 Prediction of disease-related genes and sub-networks  “ Know thy neighbors ” : search for interactors of know disease-creating genes

44. 44 A protein interaction network for Huntington disease

45. 45 Summary

46. 46 Bioinformatics is the future! Cool! Interesting! Futuristic!

47. 47 Additional courses  Perl programming for biology (2 nd semester)  Molecular Evolution  Computational Systems Biology (CS)  … and more …