초록
Various traditional optimization approaches have been applied to identify optimal manipulation strategies for metabolic networks of microorganism leading to maximization of desired products. However, because of the transient effects of traditional strategies on production rate, the design of growth-coupled production strains is essential for metabolic engineering. Most current approaches for optimal strain design problems apply a two-stage procedure to identify a growth-coupled strain. This study reformulated the optimal strain design problem as a decision making problem with a guarantee of identifying growth-coupled production strains, and a nested hybrid differential evolution (HDE) algorithm that combined the two-stage procedure into one stage was introduced to solve this problem. The performance of the proposed algorithm was demonstrated by using it to design several growth-coupled production strains for a genome-scale metabolic model of Escherichia coli iAF1260. The nested HDE was able to control the magnitude of the association between cell growth rate and chemical production rate and can outperform state-of-the-art algorithms for the design of growth-coupled strains.