27803::Systems Biology1CBS, Department of Systems Biology Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break 14:45 – 15:15Regulatory pathways lecture 15:15 – 15:45Exercise (complete previous exercises) 15:45 – 16:00 Wrap up

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

27803::Systems Biology3CBS, Department of Systems Biology Protein-DNA interactions: ChIP-chip Simon et al., Cell 2001 Lee et al., Science 2002

27803::Systems Biology4CBS, Department of Systems Biology ChIP-chip Microarray Data Differentially represented intergenic regions provides evidence for protein-DNA interaction

27803::Systems Biology5CBS, Department of Systems Biology Network representation of TF-DNA interactions

27803::Systems Biology6CBS, Department of Systems Biology Dynamic role of transcription factors Harbison C, Gordon B, et al. Nature 2004

27803::Systems Biology7CBS, Department of Systems Biology Mapping transcription factor binding sites Harbison C, Gordon B, et al. Nature 2004

27803::Systems Biology8CBS, Department of Systems Biology Affymetrix tiling arrays

27803::Systems Biology9CBS, Department of Systems Biology

27803::Systems Biology10CBS, Department of Systems Biology

27803::Systems Biology11CBS, Department of Systems Biology

27803::Systems Biology12CBS, Department of Systems Biology ChIP-Seq with Illumina (Solexa) Genome Analyzer

Integrating gene expression data with interaction networks

27803::Systems Biology14CBS, Department of Systems Biology Need computational tools able to distill pathways of interest from large molecular interaction databases Data Integration

27803::Systems Biology15CBS, Department of Systems Biology 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

27803::Systems Biology16CBS, Department of Systems Biology Network perturbations Environmental: –Growth conditions –Drugs –Toxins Genetic: –Gene knockouts –Mutations –Disease states

27803::Systems Biology17CBS, Department of Systems Biology 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)

27803::Systems Biology18CBS, Department of Systems Biology 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

27803::Systems Biology19CBS, Department of Systems Biology

27803::Systems Biology20CBS, Department of Systems Biology Network based classifier of cancer

27803::Systems Biology21CBS, Department of Systems Biology