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Prediction of the unique metabolic flux vector of Schewanella.
Utilization of Minimization of Metabolic Adjustment Principle to Estimate Genome-Wide Flux Maps
Flux balance analysis (FBA) has been applied for two decades to determine genome-wide metabolic flux distributions. However, FBA is only able to determine a solution space, instead of the unique, actual flux distribution. Minimization of metabolic adjustment (MOMA) is a decade old method to predict unique mutant fluxes from wild-type fluxes, but because unique wild-type fluxes are unknown, MOMA solutions tend to be based on the sampled FBA solution space, and are therefore solution-spaces as well.
These researchers are working to determine the unique genome-wide flux map of the wild-type Shewanella oneidensis MR-1 strain using MOMA to improve the accuracy and precision of predictive metabolic models. MOMA calculates the nearest feasible flux map in Euclidean space through quadratic programming. The Tn-Seq data acquired in the lab of Jeffrey Gralnick provides a wealth of precise mutant growth measurements. These researchers will estimate the wild-type flux map by minimizing the difference between all predicted mutant growth rates (using MOMA) and measured mutant growth rates. MOMA technology is routinely used in the Libourel lab, and the standard Matlab function “fmincon” is used to minimize the difference between observed and predicted growth rate. A subset of the mutant measurements will be left out of this analysis to compare the success in mutant prediction of the different networks used and the classical FBA approach to the proposed approach.
A bibliography of this group’s publications acknowledging MSI is attached.