원문정보
초록
영어
Reconstruction of genome-scale metabolic model and simulations have suggested a systematic prediction of gene manipulation targets. Although gene knockout targeting is relatively easy to perform, identification of gene amplification targets is complicated with more variables to consider. We report a new algorithm that incorporates genomic data into a genome-scale metabolic model for the reliable prediction of gene amplification targets. This algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints adopts genomic context and flux-converging analyses, thereby constraining reactions that co-carry flux values. This method scans changes in the variability of fluxes while the objective flux of the target chemical production is enforced from its minimal to maximal values. This strategy is expected to provide gene targets in a systematic way. [This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (NRF-2012-C1AAA001-2012M1A2A2026556) from the Ministry of Education, Science and Technology (MEST) through the National Research Foundation of Korea.]