Competitive exclusion dynamics of dual electrogenic bacteria in microbial fuel cells: A stochastic modeling under environmental perturbations

Microbial fuel cells (MFCs) utilize electrogenic bacteria to convert organic matter into electricity, providing a sustainable and environmentally friendly energy solution. S. oneidensis and G. sulfurreducens are key model organisms in MFCs research due to their distinct yet complementary electron transfer mechanisms. This study employs a Gillespie algorithm-based stochastic modeling approach to simulate the growth dynamics of these two bacteria under varying conditions. The research focuses on constructing temperature and pH-dependent growth models to analyze the impacts of these factors on bacterial populations. Simulation results align well with known optimal conditions, validating the capability of the Gillespie algorithm to respond to environmental influences. Additionally, the effects of substrate concentration and initial bacterial population ratios demonstrate how these factors affect bacterial interactions. This work demonstrates the effectiveness of the Gillespie algorithm in capturing the complex dynamics of electrogenic bacteria, offering theoretical insights into optimizing MFCs operations and advancing bio-electrochemical technologies.