Genome-scale metabolic reconstructions are the foundation for computational methods to understand and improve cellular phenotypes. While many such methods have been developed, they are generally only applied to models of single organisms. Existing methods for comparing multiple organisms via their network reconstructions can identify reconstruction differences, but not the effect these differences have on functional states of the network. We have developed an approach to identify functional differences between organisms, by finding conditions under which genetic differences give rise to differences in metabolic capabilities. We applied the algorithm to two existing reconstructions of Escherichia coli, seeking metabolic engineering strategies that give rise to different predicted biofuel production phenotypes. We were subsequently able to identify differences in the reconstructions responsible for the predicted production differences. We examine the impact of these differences on common strain design strategies, and discuss how this approach can guide selection of strains for metabolic engineering.

Comparison of microbial metabolic networks to guide background strain selection

243rd National Meeting (2012)

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