1. Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break 14:45 – 15:15Regulatory pathways lecture 15:15 – 15:45Exercise (complete previous exercises) 15:45 – 16:00 Wrap up

2. Microarrays for transcription factor binding location analysis (chIP-chip) and the “Active Modules” approach

3. Protein-DNA interactions: ChIP-chip Simon et al., Cell 2001 Lee et al., Science 2002

4. ChIP-chip Microarray Data Differentially represented intergenic regions provides evidence for protein-DNA interaction

5. Network representation of TF-DNA interactions

6. Dynamic role of transcription factors Harbison C, Gordon B, et al. Nature 2004

7. Mapping transcription factor binding sites Harbison C, Gordon B, et al. Nature 2004

8. Integrating gene Expression Data with Interaction Networks

9. Need computational tools able to distill pathways of interest from large molecular interaction databases Data Integration

10. List of Genes Implicated in an Experiment What do we make of such a result? Jelinsky S & Samson LD, Proc. Natl. Acad. Sci. USA Vol. 96, pp. 1486–1491,1999

11. KEGG http://www.genome.jp/kegg/

12. Activated Metabolic Pathways

13. 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 modifications –Protein levels –Growth phenotype –Dynamics over time

14. Network Perturbations Environmental: –Growth conditions –Drugs –Toxins Genetic: –Gene knockouts –Mutations –Disease states

15. Finding “Active” Sub-graphs Active Modules

16. Finding “Active” Modules/Pathways in a Large Network is Hard Finding the highest scoring subnetwork is NP hard, so we use heuristic search algorithms to identify a collection of high-scoring subnetworks (local optima) Simulated annealing and/or greedy search starting from an initial subnetwork “seed” Considerations: Local topology, sub-network score significance (is score higher than would be expected at random?), multiple states (conditions)

17. Activated Sub-graphs Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signaling circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S233-40.

18. Scoring a Sub-graph Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signaling circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S233-40.

19. Significance Assessment of Active Module Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S233-40. Score distributions for the 1st - 5th best scoring modules before (blue) and after (red) randomizing Z- scores (“states”). Randomization disrupts correlation between gene expression and network location.

20. Network Regions of Differential Expression After Gene Deletions Ideker, Ozier, Schwikowski, Siegel. Bioinformatics (2002)

22. Network based classifier of cancer