Outline Introduc4on to networks. Network alignment. 1 4/24/09 - PDF document

4/24/09 CSCI1950‐Z Computa4onal Methods for Biology Lecture 21 Ben Raphael April 20, 2009 hGp://cs.brown.edu/courses/csci1950‐z/ Outline • Introduc4on to networks. • Network alignment. 1

4/24/09 Signaling Networks Networks and Cancer Tumor Sequencing Project: The Cancer Genome Atlas: Muta4ons in 188 lung cancer Muta4ons in ~200 brain cancer pa4ents. pa4ents. Are these results surprising? 2

4/24/09 A quick overview of signaling networks • PDF slides. Protein Interac4on Networks • Proteins rarely func4on in isola4on, protein interac4ons affect all processes in a cell. • Forms of protein‐protein interac4ons: – Modifica4on, complexa4on [Cardelli, 2005]. phosphoryla4on protein complex 3

4/24/09 Big Problem LACK OF DATA! Pictures on previous slides summarize decades of experimental efforts. Individual genes Genomes High‐throughput DNA sequencing Individual Interac4on interac4ons Network ???? or pathways. How are protein‐protein interac4on networks derived? Yeast two‐hybrid screens 4

4/24/09 How are protein‐protein interac4on networks derived? Protein purifica4on and separa4on Protein Interac4on Networks (Over?)simplify interac4ons between proteins as a binary, sta4c rela4onship. Protein‐Protein Interac4on network – an undirected graph (usually) • Nodes: protein • Edges: interac4ons • Edges may have weights indica4ng confidence. – Yeast DIP network: ~5K proteins, ~18K interac4ons – Fly DIP network: ~7K proteins, ~20K interac4ons. PPI network – Human. ~20K protein. ~50K interac4ons. 5

4/24/09 Computa4onal Problems 1. Comparing Networks Across Species 2. Classifying Network Topology – Finding paths, cliques, dense subnetworks, etc. 3. Using networks to explain data – Dependencies revealed by network topology 4. Modeling dynamics of networks Alignment human mouse Sequences Evolve via subs4tu4ons Conserva4on implies func4on EFTPPVQAAYQKVVAG DFNPNVQAAFQKVVAG Networks Evolve via gain/loss of proteins or interac4ons (?) 6

4/24/09 Mo4va4on By similar intui4on, subnetworks conserved across species are likely func4onal modules Network Alignment “Conserved” means two subgraphs contain proteins serving similar func4ons, having similar interac4on profiles – Key word is similar, not iden4cal mismatch/subs4tu4on 7

4/24/09 Alignment Analogy Sharan and Ideker. Modeling cellular machinery through biological network comparison. Nature Biotechnology 24, pp. 427‐433, 2006 Earlier approaches: interologs • Inter ac4ons conserved in ortho logs – Orthology (descended from common ancestor) is a fuzzy no4on – Sequence similarity not necessary for conserva4on of func4on 8

4/24/09 Complica4ons • Protein sequence similarity not 1‐1 – Orthologs – Paralogs • Interac4on data: – Noisy – Incomplete – Dynamic • Computa4onal tractability Network Alignment Sharan and Ideker. Modeling cellular machinery through biological network comparison. Nature Biotechnology 24, pp. 427‐433, 2006 9

4/24/09 The Network Alignment Problem Given: k different interac4on networks belonging to different species, Find: Conserved sub‐networks within these networks Conserved defined by protein sequence similarity (node similarity) and interac4on similarity (network topology similarity) PathBLAST • Goal: iden4fy conserved pathways (chains) • Idea: can be done efficiently by dynamic programming if networks are DAGs A B C D A X’ D’ ’ Score: match + gap + mismatch + match Kelley et al (2003) 10

4/24/09 Why paths? PathBLAST (Kelley, et al. PNAS 2003) • Find conserved pathways in protein interac4on maps of two species • Model & Scoring: (Whiteboard) 11