Extracellular fluxes and gene expression data are easily measured and have been obtained for a wide variety of organisms and applications; however, it is difficult to incorporate these data directly into computational models to predict intracellular flux distributions.  Central metabolic fluxes can be quantified using 13C metabolic flux analysis (MFA), which has the advantage of providing exact fluxes which can be directly incorporated into metabolic models.  However, MFA data can be somewhat difficult and costly to obtain. We have developed a novel constraint-based modeling approach to calculate intracellular flux distributions using easily obtained experimental data from multiple genetic perturbations of a cell.  By incorporating biomass growth rates, extracellular fluxes, and gene expression data for multiple knockout strains into constraint-based metabolic models, we can more accurately predict the flux distribution in the parental strain.  This parental strain flux distribution can then be used with existing computational methods (e.g., MOMA, ROOM, and RELATCH) to predict fluxes in new mutant strains with greater accuracy, thus improving computational strain design for metabolic engineering applications.

Predicting metabolic fluxes using mutant phenotypes

247th National Meeting (2014)

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