Collaborative Research: RUI: Investigating microbial metabolic and regulatory diversity by modeling gene activity states inferred from transcriptome data

Hope College, Holland MI

Investigators

Abstract

Microorganisms have profound impacts on the environment, society and the ways that people interact with the world. This research will advance understanding of bacterial microorganisms by developing new computational technologies for combining very large sets of diverse data with models of how bacteria function in different environments. The results should allow for more accurate prediction of bacterial metabolism. The project will train a cadre of undergraduate students in mathematics, statistics, computer science, and life science and prepare them for careers in STEM fields. The methodological advancements from this work will be available for use by microbiologists and engineers to help them design new applications and processes that make use of microbial metabolic reactions. Breakthroughs in sequencing technology have set the stage for genome-scale understanding of microbial life. The next great challenge is to capture gene regulatory information in order to more accurately model the metabolic response of an organism to its environment. Prior work has developed and applied automated methods to create genome-scale metabolic models and developed a robust Bayesian statistical framework for estimating gene activity states of bacteria. These estimates are used as constraints in an integrated metabolic and regulatory model (iMRM), effectively incorporating information from large scale expression data into metabolic models. Additional work is needed to integrate such metabolic models with a wide range of alternative data sources, including transcriptional regulatory networks. To meet this need, this project will apply computational approaches for iterative cycles of model generation, experimental validation and feedback to generate better model outcomes. The results are expected to establish a rigorous statistical foundation for the generation and application of iMRMs by the scientific community. Further, the project incorporates undergraduate students in all aspects of the research as the primary research students.