High-throughput techniques in biology

1. Extracting information from complex networksFrom the metabolism to collaboration networksRoger GuimeràDepartment of Chemical and Biological EngineeringNorthwestern UniversityBloomington, April 11th, 2005

2. High-throughput techniques in biology Metabolic network Protein interactions in fruit fly Giot et al., Science (2003)

3. Large databases for critical infrastructures World-wide airport network

4. Large databases for social networks Collaborations in the Astronomical Journal Collaborations in Econometrica

5. What do “statistical properties” tell us about the network?

6. What are the important cities in the world-wide airport network? Most central cities Most connected cities

7. Cartography of complex (metabolic) networkswith L. A. N. Amaral

8. Cartography of complex (metabolic) networks • Modules One divides the system into “regions” • Roles One highlights important players

9. Real metabolic networks are extremely complex…

10. …and “regions” are not so well defined Metabolic network of E. coli

11. One can define a quantitative measure of modularity High modularity Low modularity Newman & Girvan, PRE (2003)

12. One can define a quantitative measure of modularity Ds: expected fraction of links within module s,for a random partition of the nodes ds: fraction of links within module s Modularity of a partition: M = (ds – Ds) Newman & Girvan, PRE (2003); Guimera, Sales-Pardo, Amaral, PRE (2004)

13. We use simulated annealing to obtain the partition with largest modularity Simulated Annealing

14. The new algorithm for module detection outperforms previous algorithms

15. Now we need to identify the role of each node

16. We define the within-module degree and the participation coefficient • Within-module relative degree • k: number of links of a node to other nodes in the same module • Within-module degree: • Participation coefficient • fis: fraction of links of node i in module s • Participation coefficient:Pi = 1 - fis 2

17. The within-module degree and the participation coefficient define the role of each node

18. We define seven different roles Ultra-peripheral Kinless hubs Hubs Provincial hubs Connector hubs Peripheral Kinless non-hubs Non-hub connectors Non-hubs

19. The cartographic representation of the metabolic network of E. coli Guimera & Amaral, Nature (2005)

20. The loss rate quantifies the importance of a role Metabolite Role in Species A Role in Species B A Ultra-peripheral Peripheral B Connector hub Connector hub C Ultra-peripheral LOST D LOST Peripheral ... Loss rate of role R: ploss(R) = p(lost | R)

21. Non-hub connectors are more conserved across species than provincial hubs • Comparison between 12 organisms: • 4 archaea • 4 bacteria • 4 eukaryotes Ultra-peripheral Peripheral Non-hub connectors Provincial hubs Connector hubs

22. Different networks have different role structures • 1 – Ultra-peripheral • 2 – Peripheral • 3 – Non-hub connectors • 5 – Provincial hubs • 6 – Connector hubs

23. Collaboration networks: Team assembly, network structure, and performancewith B. Uzzi, J. Spiro, and L. A. N. Amaral

24. Different collaboration networks have different properties Collaborations in the Astronomical Journal Collaborations in Econometrica

25. How do collaboration networks grow? How are teams assembled? ? • A model for collaboration network formation must specify what rules determine the participation of an individual in a team ? ? ?

26. Balancing expertise and diversity Expertise Diversity But: Need to incorporate new people But: It is easier to work with similar people and with former collaborators Performance

27. Assembling a new team 1 4 3 2 5 2 1 1-p 5 3 p 4 ? Newcomers Incumbents

28. Assembling a new team 1 4 3 2 5 2 1 5 3 4 4 Incumbents

29. Assembling a new team 1 4 3 2 5 2 1 5 1-p 3 4 4 p Newcomers Incumbents ?

30. Assembling a new team 1 4 3 2 5 4 Newcomers 6

31. Assembling a new team 1 4 3 2 5 2 1 5 1-p 3 4 4 p Newcomers Incumbents 6 ?

32. Assembling a new team 1 4 3 2 5 2 1 5 3 4 4 Incumbents 6 ?

33. Assembling a new team 1 4 3 2 5 2 1 5 5 1-q q 3 4 3 4 Any incumbent Repeat collaboration 6 ?

34. Assembling a new team 1 4 3 2 5 5 3 4 Repeat collaboration 3 6

35. Assembling a new team 1 4 6 3 1 4 2 5 3 2 5 4 3 6

36. The structure of the network depends on the fraction of incumbents... Guimera, Uzzi, Spiro & Amaral, Science (forthcoming 2005)

37. ...and on the tendency to repeat past collaborations The size of the “invisible college” increases with the fraction of incumbents, p, and decreases with the tendency to repeat collaborations, q.

38. Most fields have very similar values of p and q

39. The fraction of incumbents is positively correlated with the impact factor of journals

40. The tendency to repeat collaborations is negatively correlated with the impact factor of journals

41. Conclusions • We need to go one step further in the analysis of complex networks, so that we can provide specific answers to specific problems. • Modules and roles give important information about the structure of a network and about the importance of each node. • Networks with different functions have different role structure. • In creative collaboration networks, the emergence of the invisible college and team performance are correlated to expertise and diversity (in a “network sense”), and there may be a universal optimum.

42. Acknowledgements • Marta Sales-Pardo, André A. Moreira, and Daniel B. Stouffer. • Fulbright Commission and Spanish Ministry of Education, Culture, and Sports. More information: http://amaral.northwestern.edu/roger/ http://amaral.northwestern.edu/