文献类型：期刊文献

英文题名：Optimizing Enzyme-Constrained Metabolic Models by an Indicator Guided Bi-Surrogate Adaptive Multimodal Multiobjective Optimization Algorithm

作者：Wang, Le[1]; Fan, Xingcun[1]; Yan, Xuefeng[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China

年份：2025

外文期刊名：IEEE Transactions on Evolutionary Computation

收录：EI(收录号：20254519478779)

语种：英文

外文关键词：Constrained optimization - Energy utilization - Fuel additives - Metabolic engineering - Metabolism - Multiobjective optimization

摘要：Optimization of the enzyme turnover rate kcat in enzyme-constrained metabolic models, such as S. cerevisiae’s ecYeastGEM, is essential to improve the accuracy of cellular metabolism prediction. This paper constructs a constrained multiobjective optimization problem centered on optimizing kcat, aiming to minimize model calculation error and energy consumption while considering the maximization of specific growth rate. The problem has up to 4261 dimensions and is subject to several stringent constraints, and the existence of multiple feasible solutions for kcat significantly increases the difficulty of the optimization. To address these challenges, we propose the indicator guided bi-surrogate adaptive multimodel multiobjective evolutionary algorithm (IGSMMOEA). The algorithm approximates the constrained optimization landscape via the surrogate model, combines the indicator guided mechanism to achieve adaptive model updating, and ensures that the optimized solution satisfies the enzyme constraints through secondary validation. Experimental results demonstrate that IGSMMOEA exhibits strong generality and efficiently optimizes kcat. The optimized solutions not only surpass existing methods in diversity and convergence but also strictly adhere to biochemical constraints. The parameters derived from the algorithm are more robust, offering valuable support for metabolic engineering design. ? 1997-2012 IEEE.