earticle

영어

In the field of metabolic engineering, one of the primary goals is to maximize the production of a desired substance. However, to identify the set of gene deletions that will result to the desired production goals is difficult. This is due to the complexity of the regulatory cellular and metabolic network and also lack of good modeling and optimization tools. In this study, the optimization algorithm from previous works was implemented to identify the gene knockout on the result. The previous works faced the problem of inability to provide single run with two goals to maximize the biomass and the desired products. Besides that, the previous work also showed long computational time. In this study, a hybrid of Differential Evolution and Flux Balance Analysis (DEFBA) is proposed to solve the long computational time problem and provide an optimal set of gene knockout with high yield of the desired product. The case study in this research involved the production of succinic acid (also called as succinate) in yeast Saccharomyces cerevisiae. The results from this experiment included the list of knockout genes and the growth rate after the deletion. DEFBA had shown better results compared to the other methods. The identified list suggested gene modifications over several pathways which may be useful in solving challenging genetic engineering problems.