281946 Estimation of Genome-Scale Metabolic Fluxes From Tn-Seq Single Mutant Growth Measurements

Wednesday, October 31, 2012: 1:42 PM
Westmoreland East (Westin )
Hong Yang1, Elias W. Krumholz1, Evan D. Brutinel2, Jeffrey A. Gralnick2 and Igor G.L. Libourel1, (1)Department of Plant Biology, University of Minnesota, St. Paul, MN, (2)Department of Microbiology, University of Minnesota, St. Paul, MN

Metabolic flux is the key to understand both wild-type and mutant metabolic phenotypes. Minimization of metabolic adjustment (MOMA) has been extensively used to estimate mutant flux maps by choosing the nearest feasible flux solution to a wild-type flux map. However, a genome-wide wild-type flux map is never available, and an FBA solution space is usually used instead of a unique solution. MOMA predictions are therefore a solution space as well, where each prediction is a function of the location in the FBA solution space from which the MOMA projection is made.  

To overcome this limitation, we developed a novel algorithm to calculate the wild-type flux map by minimizing the difference between predicted mutant growth rates (using MOMA) and measured mutant growth rates from Tn-seq data. Using the estimated wild-type flux solution, we compared mutant growth predictions for a control set to predictions made by sampling the FBA solution space. The Tn-seq data was acquired for the Shewanella oneidensis strain. More generally, this work provides a new approach to calculate the unique wild-type flux map which provides better predictions for mutant phenotypes.

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