Optimization of ethanol production process by integrating Lotka-Volterra population dynamics and metabolic network models

Optimization of bacterial metabolic pathways to enhance the production of products such as ethanol has been one of the hot topics of research in the field of vitality and metabolism. As an important biofuel, ethanol has a wide range of potential applications. However, conventional metabolic network models, which take into account group performance, resource consumption, and metabolic competition, are often unable to fully reflect the complexity of the actual production process. In this study, we proposed a new strategy for optimizing the production of ethanol based on group performance by combining a group dynamics model (Lotka-Volterra model) with a metabolic network model. By simulating bacterial growth, ethanol production, and glucose consumption under different conditions, we used a grid search and genetic algorithm to optimize a number of key parameters in the model, including the bacterial growth rate, ethanol consumption rate, and ethanol production efficiency. Experimental results showed that the optimized parameter combinations were able to significantly increase the production of ethanol. Ultimately, parameter optimization based on genetic algorithms provides a theoretical basis for high-efficiency production of ethanol, and is a valuable guide to the industrial production of biofuels.