Discovery of Complex Recurring Protein Interaction Patterns within Interactomes: Algorithms, Applications and Software

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A grant has been awarded to Princeton University to develop computational tools to facilitate research in biological networks through discovery and analysis of recurring patterns of relations among biological units such as proteins. Searching for recurring patterns in biological data has been the backbone of much research and analysis in computational biology, and has been essential in uncovering biological function. In the last few years, there has been an explosion in the availability of protein-protein interaction data, and we now have large-scale interaction networks for human and many model organisms.The overall goal of this research is to develop the necessary computational infrastructure for applying recurring pattern analysis---which has already proven to be useful in analyzing biological sequence, structure and expression data---to biological networks. The specific goals of this research are: (1) To develop a computational framework for uncovering what types of proteins preferentially work together, with the goal of revealing patterns underlying cellular organization and protein functioning. (2) To apply these algorithms to existing interaction networks across the evolutionary range, with the goals of understanding how the recurring network units within organisms differ and of revealing how new types of proteins are incorporated into existing networks. (3) To develop software that, given a particular protein sequence, uncovers the recurring network interaction patterns it participates in, with the goal of placing the input protein within the context of its cellular pathways and modules, and thereby gaining insight into its function. The proposed research is coupled with the development of a new undergraduate course in bioinformatics, with a final project focusing on network analysis. Software and results of this project will be available from the website http://compbio.cs.princeton.edu.