(504a) Model-Driven Analysis of Experimental Datasets Provides Insights Into Cellular Environments and Behaviors

Reed, J. L., University of Wisconsin-Madison

Genome-scale networks of metabolism and regulation can be reconstructed from an organism's genome sequence and annotation. Models generated from these reconstructions can be used to integrate and analyze different types of experimental data in order to generate hypotheses about biochemical network structure, interactions between organisms and their environments, and responses to genetic and environmental perturbations. Examples from modeling different bacteria (Shewanella oneidensis and Escherichia coli) with biotechnology and metabolic engineering applications will be presented to illustrate how modeling and experimental efforts can be combined to improve computational models and our understanding of cellular metabolism and regulation. Model-driven analysis of gene expression data for example can be used to identify inefficient utilization of metabolism and phenotypic data can be used to refine metabolic and regulatory models and discover new connections in biological networks.