원문정보
초록
영어
The advent of in silico genome-scale model developed various algorithms to apply to metabolic engineering. Flux balance analysis (FBA) optimizes a specific objective function by linear programming under pseudo-steady state based on the stoichiometry of metabolic reactions. In order to incorporate the physiological characteristics of the organisms under gene knock-out conditions, algorithms such as MOMA and ROOM were developed. However, to improve a strain for biochemical production, the organism should be investigated from diverse sides simultaneously: for instance, biomass formation, biochemical production, and waste formation. In this respect, we propose a new approach called the flux scanning with compromised objective fluxes (FSCOF), multi-objective algorithm.
FSCOF can identify the correlation between the biomass formation, biochemical production, and waste formation by maximizing the biomass formation subjected to limiting the levels of biochemical production or waste formation. [This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Koreagovernment (MOST) (No.
M10309020000-03B5002-00000). Further supports by LG Chem Chair Professorship, Microsoft and IBM SUR program are appreciated.]