Metabolic optimization of Vibrio natriegens based on metaheuristic algorithms and the genome-scale metabolic model

In recent years, burgeoning interest in products derived from microbial production across various sectors has significantly propelled the evolution of the field of metabolic engineering. As a Gram-negative bacterium, Vibrio natriegens is characterized by its fast growth, robust metabolic capabilities, and a broad substrate spectrum, making it a promising candidate as a standard biological host for the industrial bioproduction of metabolites. Genome-scale metabolic models (GSMMs) are mathematical representations constructed based on genome annotations and gene-protein-reaction (GPR) associations within a cell. These models enable the computational simulation of intracellular reaction flux distributions. In this study, we developed a hybrid method based on metaheuristic algorithms and the GSMM to optimize metabolism for the production of ethanol and 1,3-propanediol (1,3-PDO) as target products in Vibrio natriegens. The modified GSMM used in this study contains 1195 reactions, 1094 metabolites, and 880 genes, with the metaheuristic algorithms employed being Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The optimization results indicate that the method proposed in this study can achieve the production of ethanol and 1,3-PDO by adjusting the expression levels of just 2-4 genes, with the production of 1,3-PDO reaching its theoretical maximum. The strategies developed in this study can effectively increase the production capacity of specific target metabolites in Vibrio natriegens, serving as a starting point for metabolic engineering and providing guidance for metabolic engineering targets in practical experiments.