A Hybrid of Differential Evolution and Minimization of Metabolic Adjustment for Succinic and Ethanol Production

Abstract

Succinic acid is commonly used for flavor enhancement in food products and pharmaceutical supplements, while bioethanol is a sustainable alternative and renewable liquid fuel for solving the problems of the ongoing global oil shortage and the degradation of environmental conditions. Several conventional approaches have been developed by previous researches. However, the approaches failed to maximize the production of desired products, faced poor performance in running large-scale models, and demanded high computational time. Therefore, a hybrid of Differential Evolution and Minimization of Metabolic Adjustment (DEMOMA) is proposed to predict the gene knockout strategies for maximizing the production of succinic acid and ethanol in Escherichia coli in this chapter. Differential Evolution (DE) is proposed as a stochastic and population-based optimization approach for optimizing the collection of genes. While the Minimization of Metabolic Adjustment (MOMA), which uses quadratic programming (QP), is used to define the point in flux space nearest to the wild-type point, consistent with the gene deletion constraint. Growth rate, production rate, and knockout list are generated and used to evaluate the feasibility of DEMOMA. Finally, the results obtained are used to compare with the results from previous works such as Optimal Knockouts (OptKnock), Minimization of Metabolic Adjustment Knockout (MOMAKnock), Optimal Regulation (OptReg), and Adaptive Clonal Optimization with Minimization of Metabolic Adjustment (ACOMOMA). DEMOMA shows a better performance among the methods.