Graduate Research Training Program in Computational Analysis of Biological Networks

Biological function arises from complex interactions among components. These interactions can often be represented within the framework of networks. The discovery of scale-free networks and their ubiquity in biological processes across all scales of biological organization, from gene networks to ecosystem networks, has moved the study of biological networks to the forefront of computational biology. Their ubiquity across biological scales makes them an ideal topic for truly integrative and interdisciplinary research: The structural similarity of biological networks allows researchers across areas to exchange ideas and develop computational tools that are common to different scales of biological organization. Researchers at all scales of biological organization are faced with challenges to quantitatively predict outputs of networks. For accurate predictions, we need to know the exact topology and the dynamics of each connection. A limited availability of biological information at this point has restricted analysis of network behaviors to relatively small networks. To reconstitute larger networks from smaller ones, we need to learn how to use modularity (if it exists) to predict behavior. Many groups at the University of Minnesota focus their research on network related topics, yet there is no venue at this point for making connections between these groups. This proposal will effectively connect existing forums and serve four goals: (1) graduate training in computational analysis of biological networks, (2) establishment of a clearing house for computational biology at the University of Minnesota, (3) building connections among faculty and graduate students interested in computational biology, and (4) linking the University of Minnesota research community to industry. To reach these goals, we propose a year-long graduate seminar series that will include invited speakers and the establishment of a web page that will serve as a clearing house for computational biology at the University of Minnesota.