Flux variability scanning based on enforced objective flux for identifying gene amplification targets

We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via “GR constraints”. This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation.FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.One of the most ambitious goals in metabolic engineering is the design of biological systems based on in silico predictions using mathematical models. The advent of high-throughput technologies and the completion of genome sequencing for many organisms have led to an explosion of systems-wide biological data [1,2]. Genome-scale stoichiometric models of the increasing number of microorganisms and mammalian cells have been developed at the moment [3,4]. Some of such models have been used to identify gene knockout targets for the efficient productio