(506h) Novel Tools in Genome-Scale Metabolic Flux Modeling to Identify Metabolic Engineering Targets and Predict Microbial Phenotypes

Several new tools have been developed that further enable genome-scale metabolic flux modeling to (i) design metabolic engineering strategies, (ii) select microbial hosts and formulate culture media that favor desired products, and (iii) provide metabolic flux predictions that more closely resemble those determined by ¹³C-isotopomer metabolic flux analysis (¹³C-MFA).  The Flux Balance Analysis using Flux Ratios (FBrAtio) approach enables the incorporation of specific flux distributions at critical metabolite directly into the stoichiometric matrix of a genome-scale model.  Here, we provide detailed instruction on how this is implemented and provide an account on how this method was used to engineer record level cellulose accumulation in the model plant Arabidopsis thaliana.  Next, the Total Membrane Influx constrained Flux Balance Analysis (ToMI-FBA) tool will be described and used to show that (i) L-valine addition can increase isobutanol production by Bacillus subtilis, (ii) cellobiose addition increases ethanol selectivity in Clostridium acetobutylicum ATCC 824, and (iii) B. subtilis may be an optimal host for artimisinate production.  Finally, a novel tool involving minimization of the Total Unconstrained eXchange flux by a Genetic Algorithm (TUX GA) will be described and used to show that biomass equation optimization improves metabolic flux predictions.  Here, significantly improved agreement between predicted and ¹³C-MFA  determined fluxes was obtained for autotrophic and heterotrophic growth of Synechocystis PCC 6803 using the TUX GA approach.  These results were further improved by the incorporation of FBrAtio.