Genome-Scale in silico Model for E. Coli

University Of California, San Diego, La Jolla CA

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Abstract

DESCRIPTION (provided by applicant): Development of bottom-up biochemically and genetically structured network reconstructions in conjunction with models of the major cellular processes of E. coli K-12 MG1655 has been accomplished over the last twelve years with this research program. Metabolism, transcriptional regulation, and transcription/translation interactions have been illuminated. Building of component-level knowledge has provided the common theme that allowed for the analysis of individual component functions within the context of the entire cellular system. The resulting network reconstructions and in silico models are now in wide use in the scientific community. This program proposes continuation built around three specific aims. First, additional cellular processes targeted for continuing and new reconstruction; including metabolism, transcription & translation, transcriptional regulation and two-component signaling. This is a continuation of previous work. Second, we propose the reconstruction of the protein interaction network in E. coli and its integration with the networks under aim #1, that includes, protein complex formation, the kinase and phosphatase networks coupled with their associated targets, and the protein- protein interaction networks. This is a new and novel effort. Third, we will map the E. coli K-12 MG1655 networks onto pathogenic E. coli strains molecular. Predictive power to suggest optimal nutritional conditions for colonization of host tissues and suppressive pathogenic strains growth conditions will also be addressed by the reconstructions. Accomplishment of research aims will lead to significant improvements in computational organismal models. Importance of these reconstructions is demonstrated by the current extensive use of these reconstructions models by the scientific community. Integrated networks will provide an expanded container within which the scientific communitys -omics data can be analyzed revealing an even more detailed insight into workings of cellular function. Final application is the development of reconstructions for pathogenic strains to provide a complete and accurate framework for modeling bacterial pathogenesis.