Flux balance analysis (FBA) is proving to be a powerful tool for analyzing metabolic networks. With the rapid publication of the genome sequences of a variety of organisms, the potential of FBA is only increasing. Although FBA is predicated on relatively straightforward mathematical principles, the implementation and use of FBA is not trivial. As a result, a significant hurdle is placed in the path of researchers who would like to use this approach, especially if those researchers do not have a strong quantitative background to begin with. The overarching objective of this work is to develop a new research technology which, when given an appropriately annotated genome, will allow any researcher to carry out genome-scale flux balance analysis. Specifically, upon completion of this project, a researcher will be able to use this tool to (1) determine the fluxes for all the metabolic reactions in an organism;(2) generate phenotypic phase plane plots;(3) quantitatively evaluate how probable a given metabolic objective function is relative to other objective functions;and (4) quantitatively evaluate how probable a given metabolic network is relative to other networks. In all cases, it will be possible for the researcher to customize their calculations based on their own or publicly available data. The proposed research technology will have significant biomedical benefits, particularly in regard to the analysis of microbial pathogens. They include the ability to rapidly identify key metabolic pathways in pathogens that are potential drug targets, the ability to elucidate the mechanism of action of new drugs, the ability to carry out in silico screening of new drugs to focus on the experimental development of the most promising ones, and enhance the ability to perform basic fundamental quantitative biological research, particularly for researchers who might lack the appropriate mathematical background to normally carry out such research. Finally this work should significantly enhance the national and international medical informatics and bioinformatics infrastructure.
| Bautista, Eddy J; Zinski, Joseph; Szczepanek, Steven M et al. (2013) Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum. PLoS Comput Biol 9:e1003208 |
| Jain, Rishi; Srivastava, Ranjan (2009) Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli. BMC Syst Biol 3:121 |
