Genome-scale models can be built semi-automatically through a process that includes three main stages. First, an initial genome-scale reconstruction is created utilizing all current annotation information. Second, the draft reconstruction is curated through an examination of the available literature, and the mathematical model is generated. Finally, a comparison of the in silico results to the experimental information is assessed. Here, we apply a semi-automated curation approach for the fundamental study of the metabolism of Bacillus anthracis Sterne.The curation algorithm integrates a genetic algorithm (GA) approach with flux balance analysis (FBA) and is referred to as GAFBA. The premise of the GAFBA method is based upon the observation that the optimization of a genome-scale metabolic model often results in no feasible solution or inaccurate results when they are compared to experimental information. This generally indicates that some of the constraints cannot be satisfied. The GAFBA method concentrates on the analysis of the mass balanced constraints that may be relaxed to identify feasible solutions to the FBA optimization problem. If relaxing a selected constraint allows the problem to be solved, it is likely that the associated metabolite was participating in an unaccounted for reaction. As a result, the metabolite and the potential reactions it may participate in should be reviewed. GAFBA employs a genetic algorithm to identify the minimum number of constraints that could be relaxed in order to achieve a feasible solution to the FBA optimization problem.In this work, the metabolic network of B. anthracis, the causative agent of anthrax was developed by compiling existing enzymatic data, employing genome-scale bioinformatics-driven homology searches, referencing the metabolism of other closely related Bacillus strains, generating a comprehensive biomass equation, and finally analyzing how all of the metabolites in the system interacted with each other using the GAFBA algorithm.

Developing a genome-scale metabolic model of Bacillus anthracis via a semi-automated curation approach

245th National Meeting (2013)

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