A Novel Metabolic Pathway Design Method Based on Evolutionary Algorithms and Metabolic Network Evaluation

Metabolic pathway design is a fundamental aspect of metabolic engineering, playing a crucial role in the microbial synthesis of high-value compounds. While metabolic engineers recognize the prevalence of branching reactions─side reactions that divert metabolic flux toward nontarget compounds─current automated pathway design tools often focus primarily on linear pathway optimization. This focus may lead to incomplete efficiency assessments and suboptimal pathway selection due to unaccounted metabolic complexity. To address this gap, we introduce a novel metabolic pathway design method, EA-MNE (Evolutionary Algorithm-based Metabolic Network Evaluation). Within the EA-MNE method, we propose a new approach for expanding linear pathways into metabolic networks and two new evaluation criteria: (1) the number of effective branching reactions, which assesses the extent of branching impacts, and (2) the network theoretical yield, which precisely quantifies yield losses caused by branching reactions. Additionally, we integrate four key criteria─the number of effective branching reactions, network theoretical yield, network toxicity, and Gibbs free energy─for metabolic pathway design. This integrated approach provides a systematic solution for addressing branching reaction challenges, significantly improving both the accuracy of pathway evaluation and the synthetic efficiency of microbial systems.