Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains

Abstract

Metabolic engineering is highly demanded currently for the production of various useful compounds such as succinate and lactate that are very useful in food, pharmaceutical, fossil fuels, and energy industries. Gene or reaction deletion known as knockout is one of the strategies used in in silico metabolic engineering to change the metabolism of the chosen microbial cells to obtain the desired phenotypes. However, the size and complexity of the metabolic network are a challenge in determining the near-optimal set of genes to be knocked out in the metabolism due to the presence of competing pathway that interrupts the high production of desired metabolite, leading to low production rate and growth rate of the required microorganisms. In addition, the inefficiency of existing algorithms in reconstructing high growth rate and production rate becomes one of the issues to be solved. Therefore, this research proposes Dynamic Flux Variability Analysis (DFVA) algorithm to identify the best knockout reaction combination to improve the production of desired metabolites in microorganisms. Based on the experimental results, DFVA shows an improvement of growth rate of succinate and lactate by 12.06% and 47.16% respectively in E. coli and by 4.62% and 47.98% respectively in S. Cerevisae. Suggested reactions to be knocked out to improve the production of succinate and lactate have been identified and validated through the biological database.