1. Biological Networks

3. Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002

4. Building models from parts lists

5. Protein-DNA interactions Gene levels (up/down) Protein-protein interactions Protein levels (present/absent) Biochemical reactions Biochemical levels ▲ Chromatin IP ▼ DNA microarray ▲ Protein coIP ▼ Mass spectrometry ▲none Metabolic flux ▼ measurements

6. Computational tools are needed to distill pathways of interest from large molecular interaction databases Data integration and statistical mining

7. Types of information to integrate Data that determine the network (nodes and edges) –protein-protein –protein-DNA, etc… Data that determine the state of the system –mRNA expression data –Protein levels –Dynamics over time

8. Networks can help to predict function

9. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002 Systematic phenotyping of 1615 gene knockout strains in yeast Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents) Screening against a network of 12,232 protein interactions

10. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002

11. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002

12. Networks can help to predict function Begley TJ, Mol Cancer Res. 2002.

13. Networks Topology

14. gene A gene B regulates gene A gene B binds gene A gene B reaction product is a substrate for regulatory interactions (protein-DNA) functional complex B is a substrate of A (protein-protein) metabolic pathways Network Representation nodeedge

15. Paths: metabolic, signaling pathways Cliques: protein complexes Hubs: regulatory modules Network Analysis nodeedge

16. Small-world Network Social networks, the Internet, and biological networks all exhibit small-world network characteristics Every node can be reached from every other by a small number of steps

17. Shortest-Path between nodes

19. Longest Shortest-Path

20. Small-world Network Social networks, the Internet, and biological networks all exhibit small-world network characteristics Every node can be reached from every other by a small number of steps Small World Networks are characterized by high clustering coefficient and low mean- shortest path length

21. Scale Free Networks

22. Scale-Free Networks are Robust Complex systems (cell, internet, social networks), are resilient to component failure Network topology plays an important role in this robustness –Even if ~80% of nodes fail, the remaining ~20% still maintain network connectivity –Network is very sensitive if the hubs are “attacked” In yeast, only ~20% of proteins are lethal when deleted,

23. Features of cellular Networks Cellular networks are assortative, hubs tend not to interact directly with other hubs. Hubs tend to be “older” proteins (so far claimed for protein-protein interaction networks only) Hubs also seem to have more evolutionary pressure—their protein sequences are more conserved than average between species (shown in yeast vs. worm)

24. Looking at macromolecular structures as a network How to Indentify critical position in the newtwork?

25. Searching for critical positions in a network ?

26. High degree

27. Searching for critical positions in a network ? High closeness High degree

28. Searching for critical positions in a network ? High closeness High degree High betweenness

29. Looking at macromolecular structures as a network A1191 = highest degree, closeness, betweenness A1191

30. Identifying Deleterious Mutations using a network approach Strong mutations Mild mutations 1 2

31. Identifying Deleterious Mutations p~0 p=0.01 There is a significant overlap between (predicted) functional nucleotides and critical positions of the network (high betweenness and high closeness