Large-scale measurements of molecular interactions are revolutionizing biological inquiry. A major challenge of bioinformatics over the coming years will be to develop methods for mining these interaction networks to formulate models of cellular machinery and pathways. In the present application, the multi-campus team extends the field of network integration in three fundamental directions: [a] Increases in the efficiency of pair-wise and multiple network alignment, which is a central bottleneck in network analysis; [b] Protein-network-based diagnosis of disease, through classification of expression profiles gathered for use cases and controls; and [c] Alignment of physical interaction networks with expression quantitative trait loci (eQTL) interactions, which are increasingly important for mapping signaling and regulatory pathways in mammals. Molecular interactions represent a relatively new and rapidly accumulating biological data type which has created enormous challenges and opportunities for Information Integration and Informatics (III). These data motivate a number of fundamental biological questions and associated tasks: how best to associate molecular interactions with functional roles; how to enrich for the true biological signal in often noisy interaction data; and, perhaps most importantly, how to organize interaction data into models of cellular signaling and regulatory machinery. A major outcome of the proposed research will be a suite of biological network analysis tools that are widely distributed for academic, research, and non-commercial purposes. These tools are aimed at changing the way biologists use and interpret molecular interaction data, and they will be key components of a next-generation bioinformatics toolbox for synthesizing and distilling large genomic and proteomic data sets into functional models of basic cellular processes and disease. More broadly still, tasks such as network integration and alignment are basic operations in many non-biological domains such as circuit analysis, structural chemistry, and social networks, such that the proposed advances may have far-reaching implications. Network analysis algorithms and software will be used to develop a lecture course module for graduate instruction in degree programs in Bioinformatics and Computer Science. Other broad impacts stemming from this project include: fostering research opportunities for graduate students; educating the biological community in the significance and use of new methods through research publications, the popular press, and an interactive website; providing software to supplement coursework in bioinformatics; and enhancing collaborative ties between the areas of Computer Science and Bioengineering.
