A new strategy integrating genetic algorithms with flux balance analysis (GAFBA) was developed to aid in fundamental studies of metabolism, as well as to facilitate curation of genome-scale metabolic networks. The premise for this method was based on the observation that optimization of genome-scale metabolic models often results in no feasible solution. It was hypothesized that metabolic constraints were either missing or incomplete.  To determine which metabolic constraints were not fulfilled, GAFBA evolved metabolic models by relaxing various mass balance constraints.  As a result, metabolites potentially involved in undocumented reactions were identified.To validate the methodology, a genome-scale metabolic model for Mycoplasma gallisepticum was generated. After GAFBA analysis, the model predicted an average growth rate of 0.322�0.12 h-1, closely matching the experimentally determined growth rate of 0.244�0.03 h-1 . To accomplish this feat, new pathways not predicted from genome annotation were identified using the algorithm and validated based on data in the literature.

Use of a hybrid genetic algorithm/flux balance analysis approach to identify metabolic pathways not predicted by genome annotation

243rd National Meeting (2012)

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