Seminar Abstract:
As the dominant form of life on our planet, microbes are deeply involved in the environment, health, and the generation of bioenergy. The rise of genomics has given us a new lens to study the microbial world – providing a torrent of data that requires deep network contexts to interpret biologically. To harness the power of genomics, it is thus essential to link genotype to phenotype through the construction of quantitative systems models. I will discuss approaches for the creation of such quantitative models that can simulate a variety of microbial functions, and show examples of a number of such models we have built in my lab. I will focus particularly on automated methods for building metabolic and regulatory networks of microbes such as our newly developed approach, Probabilistic Regulation of Metabolism (PROM) (Chandrasekaran and Price, PNAS, 2010). PROM is notable in that it represents the successful integration of a top-down reconstructed, statistically inferred regulatory network with a bottom-up reconstructed, biochemically detailed metabolic network, bridging two important classes of systems biology modelsthat are rarely combined quantitatively.