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

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

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

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. Affymetrix tiling arrays

12. ChIP-Seq with Illumina (Solexa) Genome Analyzer

13. Integrating gene Expression Data with Interaction Networks

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

15. List of Genes Implicated in an Experiment
Jelinsky S & Samson LD,
Proc. Natl. Acad. Sci. USA
Vol. 96, pp. 1486–1491,1999
How do we interpret these results?

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

17. Activated Metabolic Pathways

18. 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

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

20. Finding Activated Sub-graphs
Active Modules

21. Finding Activated Modules/Pathways in a Large Network is Hard
Finding the highest scoring sub-network is NP hard, so we use heuristic search algorithms to identify a collection of high-scoring sub-networks (local optima)
Simulated annealing and/or greedy search starting from an initial sub-network “seed”
Considerations: Local topology, sub-network score significance (is score higher than would be expected at random?), multiple states (conditions)
So now that we have a scoring system, we can turn to the problem of finding the high-scoring pathways themselves.

22. 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:S

23. 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:S

24. Significance Assessment of Active Module
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.
Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S

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

27. Network based classifier of cancer