Optimizing Enzyme-Constrained Metabolic Models by an Indicator Guided Bi-Surrogate Adaptive Multimodal Multiobjective Optimization Algorithm
针对酵母酶约束代谢模型，提出一种指标引导的双代理自适应多模态多目标进化算法，优化酶周转率以同时降低模型计算误差和能耗，并最大化比生长速率，实验表明算法在多样性和收敛性上优于现有方法。
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.