1. Protein network analysis Network motifs Network clusters / modules Co-clustering networks & expression Network comparison (species, conditions) Integration of genetic & physical nets Network visualization

2. www.cytoscape.org OPEN SOURCE Java platform for integration of systems biology data Layout and query of networks (physical, genetic, social, functional) Visual and programmatic integration of network state data (attributes) The ultimate goal is to provide tools to facilitate all aspects of network assembly, annotation, and use in biomedicine. RECENT NEWS Version 2.7 released March 2010 Cytoscape ® Registered Trademark The Cytoscape Consortium is a 501(c)3 non-for-profit in the State of California Centerpiece of the new National Resource for Network Biology, $7 million from NCRR Downloaded approximately 3000 times per month Shannon et al. Genome Research 2003 Cline et al. Nature Protocols 2007

3. Cytoscape Plugin Usage Statistics

4. Integration of networks and expression

5. Querying biological networks for “Active Modules” Ideker et al. Bioinformatics (2002) Interaction Database Dump, aka “Hairball” Active Modules Color network nodes (genes/proteins) with: Patient expression profile Protein states Patient genotype (SNP state) Enzyme activity RNAi phenotype

6. A scoring system for expression “activity” ABCD

7. Scoring over multiple perturbations/conditions Perturbations /conditions

8. Searching for “active” pathways in a large network Score subnetworks according to their overall amount of activity Finding the highest scoring subnetworks is NP hard, so we use heuristic search algs. to identify a collection of high-scoring subnetworks (local optima) Simulated annealing and/or greedy search starting from an initial subnetwork “seed” During the search we must also worry about issues such as local topology and whether a subnetwork’s score is higher than would be expected at random

9. Simulated Annealing Algorithm

10. Network regions whose genes change on/off or off/on after knocking out different genes

11. Initial Application to Toxicity: Networks responding to DNA damage in yeast Tom Begley and Leona Samson; MIT Dept. of Bioengineering Systematic phenotyping of gene knockout strains in yeast Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents) Sensitive Not sensitive Not tested MMS sensitivity in ~25% of strains Screening against a network of protein interactions…

12. Begley et al., Mol Cancer Res, (2002)

13. Networks responding to DNA damage as revealed by high-throughput phenotypic assays Begley et al., Mol Cancer Res, (2002)

14. Host-pathogen interactions regulating early stage HIV-1 infection Genome-wide RNAi screens for genes required for infection utilizing a single cycle HIV-1 reporter virus engineered to encode luciferase and bearing the Vesicular Stomatitis Virus Glycoprotein (VSV-G) on its surface to facilitate efficient infection… Sumit Chanda

15. Project onto a large network of human-human and human-HIV protein interactions

16. Network modules associated with infection Konig et al. Cell 2008

17. Network-based classification

18. NETWORK-BASED CLASSIFICATION Disease aggression (Time from Sample Collection SC to Treatment TX) Chuang et al. MSB 2007 Lee et al. PLoS Comp Bio 2008 Ravasi et al. Cell 2010

19. The Mammalian Cell Fate Map: Can we classify tissue type using expression, networks, etc? Gilbert Developmental Biology 4 th Edition

20. Interaction coherence within a tissue class B B A A B B A A B B A A Endoderm Mesoderm Ectoderm (incl. CNS) r = 0.9 r = 0.0 r = 0.2 Taylor et al. Nature Biotech 2009

21. Protein interactions, not levels, dictate tissue specification

22. Functional Enrichment

23. Gene Set Enrichment Analysis - GSEA - ::: Introduction. MIT Broad Institute v 2.0 available since Jan 2007 v 2.0.1 available since Feb 16th 2007 Version 2.0 includes Biocarta, Broad Institute, GeneMAPP, KEGG annotations and more... Platforms: Affymetrix, Agilent, CodeLink, custom... GSEA (Subramanian et al. PNAS. 2005.)

24. GSEA applies Kolmogorov-Smirnof test to find assymmetrical distributions for defined blocks of genes in datasets whole distribution. Gene Set Enrichment Analysis - GSEA - ::: Introduction. Is this particular Gene Set enriched in my experiment? Genes selected by researcher, Biocarta pathways, GeneMAPP sets, genes sharing cytoband, genes targeted by common miRNAs …up to you…

25. Dataset distribution Number of genes Gene Expression Level Gene Set Enrichment Analysis - GSEA - ::: Introduction. ::: K-S test The Kolmogorov–Smirnov test is used to determine whether two underlying one-dimensional probability distributions differ, or whether an underlying probability distribution differs from a hypothesized distribution, in either case based on finite samples. The one-sample KS test compares the empirical distribution function with the cumulative distribution functionspecified by the null hypothesis. The main applications are testing goodness of fit with the normal and uniform distributions. The two-sample KS test is one of the most useful and general nonparametric methods for comparing two samples, as it is sensitive to differences in both location and shape of the empirical cumulative distribution functions of the two samples. Gene set 1 distribution Gene set 2 distribution

26. ClassA ClassB ttest cut-off FDR<0.05...testing genes independently... Biological meaning? Gene Set Enrichment Analysis - GSEA - ::: Introduction.

27. Correlation with CLASS - + ClassA ClassB Gene Set 1 ttest cut-off Gene Set 2 Gene Set 3 Gene set 3 enriched in Class B Gene set 2 enriched in Class A Gene Set Enrichment Analysis - GSEA - ::: Introduction.

29. Subramaniam, PNAS 2005

30. NES pval FDR Gene Set Enrichment Analysis - GSEA - ::: Introduction. The Enrichment Score ::: Benjamini-Hochberg

31. Network Alignment Species 1 vs. species 2 Physical vs. genetic

32. Kelley et al. PNAS 2003 Ideker & Sharan Gen Res 2008 Cross-comparison of networks: (1) Conserved regions in the presence vs. absence of stimulus (2) Conserved regions across different species Sharan et al. RECOMB 2004 Scott et al. RECOMB 2005Sharan & Ideker Nat. Biotech. 2006 Suthram et al. Nature 2005

33. Conserved Plasmodium / Saccharomyces protein complexes Plasmodium-specific protein complexes Suthram et al. Nature 2005 La Count et al. Nature 2005 Plasmodium: a network apart?

34. Human vs. Mouse TF-TF Networks in Brain Tim Ravasi, RIKEN Consortium et al. Cell 2010

35. Finding physical pathways to explain genetic interactions Adapted from Tong et al., Science 2001 Genetic Interactions: Classical method used to map pathways in model species Highly analogous to multi-genic interaction in human disease and combination therapy Thousands are being uncovered through systematic studies Thus as with other types, the number of known genetic interactions is exponentially increasing…

36. Integration of genetic and physical interactions 160 between- pathway models 101 within- pathway models Num interactions: 1,102 genetic 933 physical Kelley and Ideker Nature Biotechnology (2005)

37. Systematic identification of “parallel pathway” relationships in yeast

38. Unified Whole Cell Model of Genetic and Physical interactions

39. A dynamic DNA damage module map Bandyopadhyay et al. Science (2010)