Computational Platform for Flux Analysis Using 13C-Label Tracing

75916-Advances in bioinformatics and genome research have generated a rapid expansion in the availability of information at all levels of biological investigation. One of the goals of the Department of Energy¿s Genomes to Life Initiative is the development of a computational infrastructure to allow this information to be interpreted at the whole cell level and to predict the behavior of complex systems in response to their environment. This project will develop a combined experimental/in silico simulation platform to improve predictive capabilities. Specifically, flux measurements will be integrated with gene expression data, within the context of genome-scale metabolic models, to further the ability to analyze, interpret, and predict the behavior of biological systems. A current software platform will be supplemented with the ability to analyze data from 13C-labeling experiments, and the resulting flux measurements will be interpreted in the context of a predictive genome-scale model. In Phase I, 13C-label tracing experiments will be performed on chemostat-grown cultures of wild-type and mutant Methylobacterium extorquens AM1 strains, and programs will be written to calculate the internal metabolic flux distributions from these data. A genome-scale metabolic model of this organism will also be constructed, and its predictive power will be assessed by comparison to the flux measurements and gene expression data collected in a separate experiment. Commercial Applications and Other Benefits as described by the awardee: A streamlined method for the analysis of 13C-labeling data and its interpretation in the context of a genome-scale model should make combined experimental/modeling techniques approachable to the non-expert user. The tool should have application to basic biological research, metabolic engineering efforts for the production of chemicals from renewable feedstocks, and the development of strains to sequester greenhouse gases.