Predicting and Analyzing Cellular Networks

High-throughput experimental technologies, along with computational predictions, have resulted in large-scale biological networks for numerous organisms. Global analyses of biological networks provide new opportunities for revealing protein functions and pathways, and for uncovering cellular organization principles. In my talk, I will discuss a number of approaches we have developed over the years for the complementary problems of predicting interactions and analyzing interaction networks. First, I will describe a genomic approach for uncovering high-confidence regulatory interactions, and show how it can be effectively combined with a framework for predicting regulatory interactions for proteins with known structural domains but unknown binding specificity. Next, I will describe algorithms for analyzing protein interaction networks in order to uncover protein function and functional modules, and demonstrate the importance of considering the topological structure of interaction networks in order to make high quality predictions. Finally, I will present a framework for explicitly incorporating known attributes of individual proteins into the analysis of biological networks, and utilize it to discover recurring network patterns underlying a range of biological processes.