SGER: Algorithms for predicting protein function using interaction maps

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Intellectual MeritThe goal of this project is to develop algorithms for analyzing protein interaction maps, inorder to make novel predictions about a protein's biological process. The goal is to providea framework for moving from individual pairwise linkages to exploiting entire interaction networks,where each interaction may arise from either experimental and/or other computational methods. Methods for analyzing interaction networks are in their infancy, and most current approaches predict the function of a protein by considering only the annotations of its direct interactions. In contrast, the proposed methods will use the global connectivity of interaction networks, the relationships between functions, and several high-throughput data sources in making predictions. The hope is that by developing novel network-based algorithms, we will obtain functional predictions for many, as yet, uncharacterized proteins.Broader ImpactBoth PIs teach cross-disciplinary courses in computational biology, and the research outlined here will further enhance their educational efforts. The co-PI, Chazelle, has designed and is teaching a new undergraduate course-an integrated, quantitative introduction to the natural sciences. Together with biologists, physicists, and chemists the PI, Singh, has designed a graduate course Introduction to computational molecular biology and genomics; she has co-taught it with a molecular biologist for the past four years.The proposed work will develop methods using interaction maps for baker's yeast and fruit fly. Humans share many proteins and pathways with these model organisms. Thus, network analysis methods may allowtransfer of information from these organisms to human, potentially revealing critical information about proteins and pathways implicated in human disease. Predictions and software will be made available on the web (www.cs.princeton.edu/mona/software.html).