(94e) Constraint-Based Analysis of Metabolic Capacity of Salmonella Typhimurium LT2
Computational models of microorganisms be useful for integrating and analyzing various types of data, and for making predictions about cellular behavior in different genetic or environmental backgrounds. We have developed a genome-scale metabolic reconstruction for Salmonella typhimurium LT2, a human pathogen that is closely related to Escherichia coli. The reconstructed metabolic network for S. typhimurium contains 1,087 metabolic and transport reactions connecting 759 metabolites. The resulting network accounts for nearly a quarter of the genes in the bacterium (1,083 out of 4,489). We have translated this reconstruction (iRR1083) into a constraint-based metabolic model for S. typhimurium LT2. We used this model to analyze experimental data taken from minimal media conditions, including growth rates, growth conditions, and gene essentiality. Comparisons between S. typhimurium and E. coli model predictions provides examples where these two closely related organisms differ in their metabolic capabilities and regulation of enzyme activity. In addition to minimal media conditions, we have also simulated growth in rich media (LB or a host-cell environment) to identify genes needed for virulence and to investigate which metabolic reactions could be utilized under such conditions and compared this with gene expression and proteomic measurements. We will discuss how genomic information can be used to develop models that can be used for predicting phenotypes, analyzing experimental data, and generating hypotheses about metabolism and regulation.