Abstract
Motivation: Although constraint-based flux analysis of knockout strains has facilitated the production of desirable metabolites in microbes, current screening methods have placed a limitation on the number knockouts that can be simultaneously analyzed.Results: Here, we propose a novel screening method named FastPros. In this method, the potential of a given reaction knockout for production of a specific metabolite is evaluated by shadow pricing of the constraint in the flux balance analysis, which generates a screening score to obtain candidate knockout sets. To evaluate the performance of FastPros, we screened knockout sets to produce each metabolite in the entire Escherichia coli metabolic network. We found that 75% of these metabolites could be produced under biomass maximization conditions by adding up to 25 reaction knockouts. Furthermore, we demonstrated that using FastPros in tandem with another screening method, OptKnock, could further improve target metabolite productivity.Availability and implementation: Source code is freely available at http://www-shimizu.ist.osaka-u.ac.jp/shimizu_lab/FastPros/, implemented in MATLAB and COBRA toolbox.Contact: chikara.furusawa@riken.jp or shimizu@ist.osaka-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Metabolic engineering of microbes has been successfully used for the production of a variety of useful compounds in microbes (Keasling, 2010; Shimizu, 2002; Stephanopoulos et al, 1998; Zaldivar et al, 2001)
3 RESULTS 3.1 Screening of knockout sets for target production To investigate the performance of FastPros, we selected 625 metabolites in the E.coli metabolic model and the screened reaction knockout sets that result in the production of each metabolite when the biomass production is maximized
We have developed a novel algorithm, FastPros, to screen sets of reaction knockouts that produce target metabolites
Summary
Metabolic engineering of microbes has been successfully used for the production of a variety of useful compounds in microbes (Keasling, 2010; Shimizu, 2002; Stephanopoulos et al, 1998; Zaldivar et al, 2001). Given that several individual genetic modifications are often required to improve target productivity (Becker et al, 2011; Yim et al, 2011), the selection of an appropriate set of modifications from a large number of possible combinations is challenging. To overcome this obstacle, tools based on computer simulation and mathematical modeling have been developed, which make possible screening for appropriate sets of genetic modifications to improve target productivity (Burgard et al, 2003; Patil et al, 2005; Tepper and Shlomi, 2010). In FBA, metabolic fluxes can be quantitatively estimated by assuming a steady state metabolic system and optimization of an objective function. Quantitative flux predictions by FBA can accelerate the rational design of metabolic networks to improve the yield of target products (Alper, et al, 2005; Park, et al, 2007)
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