1. 5 th Annual Cytoscape Symposium Amsterdam Medical CenterNovember 8 2007

2. Large-scale interaction technologies PHYSICALGENETIC ORDERED Cause and effect Signal transducing Protein-gene (transcriptional, ChIP-Chip 22, 39 ) Protein-RNA (RIP-chip 80 ) Protein-protein (kinase-substrate arrays 21, LUMIER 81 ) Protein-compound 82 Epistatic orderings a < b OR b < a (EMAP 28, 83 ) Knock-down expression profiles (RNAi 32, deletion mutants 36, 37 ) Expression QTLs 41, 42 UNORDERED Ambiguous directionality Protein-protein (co-IP/MS/MS 18-20, Y2H 15, 84-86 ) Gene-gene (co-regulon 87 ) Synthetic lethality ab << a, b, wt (SGA 88, dSLAM 31, 71, EMAP 28, 83, chemogenomic profiling 89 ) Beyer, Bandyopadhyay, and Ideker Nat. Rev. Genetics (2007)

3. Like sequence, protein interaction data are exponentially growing… DIP Database Growth total interactions EMBL Database Growth total nucleotides (gigabases) 198020001990 0 10 5

4. www.cytoscape.org OPEN SOURCE Java platform for integration of systems biology data Layout and query of interaction networks (physical and genetic) Visual and programmatic integration of molecular state data (attributes) The ultimate goal is to provide the tools to facilitate all aspects of pathway assembly and annotation. RECENT NEWS Version 2.5 released Summer 2007; Scalability+efficiency now equivalent to best commercial packages The Cytoscape Consortium is a 501(c)3 non-for-profit in the State of California The Cytoscape ® Registered Trademark awarded JOINTLY CODED with Agilent, ISB, Pasteur, Sloan-Kettering, UCSF, Unilever, U Toronto

5. Thinking about the parallels between processing of genomes and interactomes Beyer, Bandyopadhyay, and Ideker Nat. Rev. Genetics (2007).

6. http://gai.nci.nih.gov/Kelley and Ideker, Nat. Biotech (2005) Integration of genetic and physical maps: From the Genome (a) to the Interactome (b)

7. Trey Ideker University of California San Diego Gaining power in gene association studies with Cytoscape

8. MAPPING DISEASE GENES Gene association studies measure the association between a disease trait (e.g. diabetic / non-diabetic) and a panel of SNPs (or other polymorphic genetic markers) distributed across the genome The goal is to identify SNPs (or haplotypes) that correlate with incidence of the disease The phenotypic trait can also be quantitative, e.g. blood pressure, age, weight, body fat index These are so-called Quantitative Trait Loci

9. EXPRESSION AS A TRAIT Expression Quantitative Trait Loci (eQTLs) look for associations between SNPs and gene expression levels. All-vs-All analysis: ALL SNPs are evaluated for association with ALL gene expression levels. This process can generate thousands of associations. SNPs Genes

10. MANY CHALLENGES Fine Mapping: Due to the spacing of genetic markers and/or linkage disequilibrium, several genes can reside near each SNP marker. Typically, only one of these genes is responsible for the observed expression phenotype. Identifying the true causative gene requires additional data, since all genes at a locus are indistinguishable based on the eQTL data alone. Lack of mechanistic explanation: A gene- phenotype association typically lends little insight into the underlying molecular mechanism. Lack of statistical power: Many real gene-phenotype associations may have only weak association signals. But boosting the signal involves genotyping prohibitive numbers of individuals.

11. Knockout causes up-regulation Knockout causes down-regulation Cause and effect interactions Epistatic orderings (SGA / EMAP) Knock-down expression profiles (RNAi, deletion mutants) Expression QTLs

12. Kulp DC, Jagalur M (2006) Causal inference of regulator-target pairs by gene mapping of expression phenotypes. BMC genomics 7: 125. Lee SI, Pe'er D, Dudley AM, Church GM, Koller D (2006) Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proceedings of the National Academy of Sciences of the United States of America 103: 14062-14067. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, Lum PY, Leonardson A, Thieringer R, Metzger JM, Yang L, Castle J, Zhu H, Kash SF, Drake TA, Sachs A, Lusis AJ (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nature genetics 37: 710-717. Tu Z, Wang L, Arbeitman MN, Chen T, Sun F (2006) An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics (Oxford, England) 22: e489-496. NETWORK INFERENCE

13. Large-scale interaction technologies PHYSICALGENETIC ORDERED Cause and effect Signal transducing Protein-gene (transcriptional, ChIP-Chip 22, 39 ) Protein-RNA (RIP-chip 80 ) Protein-protein (kinase-substrate arrays 21, LUMIER 81 ) Protein-compound 82 Epistatic orderings a < b OR b < a (EMAP 28, 83 ) Knock-down expression profiles (RNAi 32, deletion mutants 36, 37 ) Expression QTLs 41, 42 UNORDERED Ambiguous directionality Protein-protein (co-IP/MS/MS 18-20, Y2H 15, 84-86 ) Gene-gene (co-regulon 87 ) Synthetic lethality ab << a, b, wt (SGA 88, dSLAM 31, 71, EMAP 28, 83, chemogenomic profiling 89 ) Beyer, Bandyopadhyay, and Ideker Nat. Rev. Genetics (2007)

14. Integration of cause-and-effect interactions with physical networks TF-promoter binding Protein-protein binding Perturbation causes up-regulation Perturbation causes down-regulation Yeang, Mak et al. Genome Biology 2005 Perturbation effects

15. Numbers of promoters bound by each of 30 transcription factors (TFs) before and after exposure to methyl-methane sulfonate (MMS)  MMS only  MMS only both A systems approach to mapping DNA damage networks Workman, Mak, et al. Science 2006

16. Integration of cause-and-effect interactions with physical networks TF-promoter binding Protein-protein binding Perturbation causes up-regulation Perturbation causes down-regulation Yeang, Mak et al. Genome Biology 2005 Perturbation effects

17. Validation of binding with knockout data yields a large regulatory network Workman, Mak, et al. Science 2006

18. Back to the main problem – interpreting eQTL associations with protein networks

20. Application to genome-wide eQTLs in yeast (Brem 2005) Associations between expression levels and 2,956 genetic markers measured across 112 yeast strains All locus–target pairs with a gene association p-value ≤ 0.05 were considered, yielding a total of 819,283 locus– target associations. These associations were provided to eQED to predict the causal gene behind each locus-target pair. eQED made 131,863 predictions of which causal gene in the locus which controlled the expression changes observed in the target.

21. Examples

22. Network example, and causal gene prediction accuracy Method Number of Correct Predictions * Random118 Tu et al.262 eQED392 eQED (multi-locus)438 * Out of 548 gold-standard cause-effect pairs compiled from yeast gene-expression knockout studies by Hughes et al. (2007) and Hu et al. (2007) and a gene over-expression study by Chua et al. (2006).

23. Conserved human/yeast MAP kinase cascades Human Yeast Collaboration with Prolexys and Burnham Institute

24. Funding: NIEHS, NIGMS, Unilever, Packard Fellowship Websites: www.cytoscape.org eQTL association: Silpa Suthram, Andreas Beyer Collaborators: Roded Sharan (Tel Aviv), Richard Karp (Berkeley) DNA Damage Networks: Craig Mak Chris Workman Collaborators: Leona Samson (MIT) Tom Begley (U Albany)

25. www.cytoscape.org OPEN SOURCE Java platform for integration of systems biology data Layout and query of interaction networks (physical and genetic) Visual and programmatic integration of molecular state data (attributes) The ultimate goal is to provide the tools to facilitate all aspects of pathway assembly and annotation. RECENT NEWS Version 2.5 released Summer 2007; Scalability+efficiency now equivalent to best commercial packages The Cytoscape Consortium is a 501(c)3 non-for-profit in the State of California The Cytoscape ® Registered Trademark awarded JOINTLY CODED with Agilent, ISB, Pasteur, Sloan-Kettering, UCSF, Unilever, U Toronto

27. http://CellCircuits.org