Central to how living cells accomplish diverse biological functions are regulatory networks that control what genes need to be activated under different environmental conditions. As evolution is the ultimate tinkerer of living systems systematic comparisons of how regulatory networks evolve to drive species-specific differences are critical to understand cellular functions. Through advances in genomics, it is now possible to measure the activity levels of almost all genes for many species. This provides a unique opportunity to systematically compare these gene activity levels across multiple organisms and link changes in activity to changes in the networks of individual species. However, this is challenging because, first, such comparisons require the regulatory networks to be known in not one but multiple species including those that are poorly characterized, and second, computational methods to compare molecular datasets across species other than DNA sequence are in their infancy. This project will address these challenges by developing novel computational methods to identify and compare regulatory networks across multiple species.
The overarching goal of this project is to establish a comparative network biology framework to study evolutionary dynamics of regulatory networks. In Aim 1, algorithms for regulatory network reconstruction that combine probabilistic models of evolution with network reconstruction will be developed, that take into account both sequence and expression determinants of regulatory networks. The network reconstruction algorithms will incorporate structural constraints of regulatory networks such as modular organization, where sets of genes are coordinately regulated to achieve specific cellular functions. In Aim 2, methods to identify and correlate regulatory network patterns of divergence to phenotypic states will be developed. In Aim 3, methods from Aims 1 and 2 will be applied to published and newly generated expression datasets in yeast and land plant phylogenies to study the evolution of stress response, carbon metabolism and plant-microbe relationships. Network predictions will be interpreted and tested by yeast and plant collaborators in the laboratory. This research project intersects several computational (graph theory, algorithms, machine learning) and biological (gene regulation and evolution) areas of research, providing an excellent training opportunity for scientists at the interface of these areas. The planned outreach activities include developing a Network biology course, and network biology boot camp for a broader audience including high school teachers. The PI will organize summer research internships in Network biology for undergraduate students from underrepresented and minority groups through the Computational Biology and Biostatistics Summer Research Opportunities (CBB SROP) at UW Madison, and participate in general public outreach from the Town Center at the Wisconsin Institute for Discovery (a public venue for regular school field trips and Science festivals). Software tools and resources developed by this proposal will be made publicly available through dedicated websites.
