1. Computational characterization of biomolecular networks in physiology and disease Kakajan Komurov, Ph.D Department of Systems Biology University of Texas MD Anderson Cancer Center

2. Classical to Systems Biology Gene 1 Function 1 Gene 2 Function 2... Gene/protein/molecule-centric research

3. Classical to Systems Biology Phenotype 1 Phenotype 2 Phenotype 3...

4. Classical to Systems Biology Phenotype 1 Phenotype 2 Phenotype 3... Systems-level analyses High throughput experiments – high content data Genomics, proteomics, metabolomis, … - “omics” fields Extensive use of computational tools Computational systems biology

5. Studying organizational principles of biological systems – Dynamic structure – function relationship in biological networks Developing computational tools to analyze/interpret large-scale data

6. Computational systems biology Studying organizational principles of biological systems – Dynamic structure – function relationship in biological networks Developing computational tools to analyze/interpret large-scale data

7. Dynamics of protein interaction networks Stimulus Protein network Gene expression program

8. Dynamics of protein interaction networks Stimulus Protein network Gene expression program Remodeling of the network

9. Dynamic organizational principles in protein networks Komurov and White (2007), Komurov, Gunes, White (2009)

10. Dynamic organizational principles in protein networks Komurov and White (2007), Komurov, Gunes, White (2009)

11. Cancer systems biology Extensive data collection at the whole-genome level – The Cancer Genome Atlas Project – Expression Oncology project – Alliance for Signaling project System-level understanding of cellular processes activated in cancer Computational methods to maximize analytic power, generate testable hypotheses

12. Biological complexity ~22,000 annotated human genes in RefSeq ~60,000 known protein-protein interactions in human Millions of indirect relationships between genes Typical genomic experiment: millions of data points

13. Objectives Analyze data within the context of a priori information – Physical interactions – Function similarity – Sequence similarity – Co-localization Extract most relevant genes/subnetworks – Genes with high data values – Coordinately regulated genes with similar functions – Genes with partially redundant functions How to score importance/relevance of a gene/subnetwork to the given experimental context?

14. NetWalk Principle: relevance of a gene depends on its measured experimental value and its connections to other relevant genes Random walk – based method for scoring network interactions for their relevance to the supplied data Simultaneously assesses the local network connectivity and the data values of genes No data cutoffs, assesses the whole data distribution

19. Transition probability Deriving node relevance scores Relevance score at step k Left eigenvector of the transition probability matrix

20. Deriving Edge Flux (EF) value Node relevance score = visitation probability

21. Deriving Edge Flux (EF) value Edge Flux Node relevance score = visitation probability

22. Too much bias towards network topology

23. Deriving Edge Flux (EF) value Edge Flux Normalized Edge Flux Node relevance score = visitation probability Background node visitation score

26. Low dose vs. high dose DNA damage

27. Statistical analyses using EF values instead of gene values Identifying link communities instead of gene communities

29. Development of drug resistance in breast cancer Lapatinib: drug that blocks activity of HER2 oncoprotein Patients with activated HER2 have good initial response to the drug, but develop resistance in a short time Our strategy: identify networks supporting the drug resistance of breast cancer cells to lapatinib

30. Cell culture model of drug resistance in breast cancer

31. SKBR3SKBR3-R SKBR3SKBR3-R+Lapatinib (1uM) Perform NetWalk analysis of gene expression data to identify most active networks in lapatinib resistance Strategy

32. Over-represented networks in lapatinib resistance

33. Drug resistance can be reversed by diabetes drugs

34. Acknowledgments Ph.D Mentor: Michael White, Ph.D Current Mentor: Prahlad Ram, Ph.D Ram lab: – Melissa Muller, Ph.D – Jen-Te Tseng – Sergio Iadevaia, Ph.D Ju-Seog Lee, Ph.D Yun-Yong Park, Ph.D Collaborators: – Luay Nakhleh, Ph.D (Rice University) – Michael Davies, M.D Ph.D (MDA) – Mehmet Gunes, Ph.D (UNR)