We study the pair dynamics of two self-propelled sphere dimers in the chemically active medium in which a cubic autocatalytic chemical reaction takes place. Concentration gradient around the dimer, created by reactions occurring on the catalytic sphere surface and responsible for the self-propulsion, is greatly influenced by the chemical activities of the environment. Consequently, the pair dynamics of two dimers mediated by the concentration field are affected. In the particle-based mesoscopic simulation, we combine molecular dynamics (MD) for potential interactions and reactive multiparticle collision dynamics (RMPC) for solvent flow and bulk reactions. Our results indicate three different configurations between a pair of dimers after the collision, i.e., two possible scenarios of bound dimer pairs and one unbound dimer pair. A phase diagram is sketched as a function of the rate coefficients of the environment reactions. Since the pair interactions are the basic elements of larger scale systems, we believe the results may shed light on the understanding of the collective dynamics.

Substrate inhibition in lactic acid bacteria (LAB) fermentation occurs when substrate concentration exceeds a critical value, leading to reduced cell growth and thus inefficient lactic acid production. Many efforts, including experimental and kinetic models, have been devoted to elucidate the possible mechanisms of substrate inhibition. However, the molecular and physiological basis of this phenomenon remains incompletely characterized. In this study, we propose a mechanistic two‐pathway model that integrates a substrate‐responsive molecular regulatory pathway into the typical substrate assimilation and microbial growth pathway. Our modeling analysis captures a global growth dynamics, including lag, exponential, and stationary phases over a wide range of initial substrate concentrations, with one set of parameters. Consequently, the results exhibit a significantly prolonged lag phase at high initial substrate concentrations. We test this model framework by combining the model results with the published experimental data of LAB batch fermentation such as Lactobacillus bulgaricus , Lactobacillus casei , and Lactiplantibacillus plantarum on lactose, demonstrating its universality beyond specific substrate‐strain systems. Furthermore, the model simulations show that an appropriate preculture treatment for modulating the inoculum’s physiological state of the population could be a possible approach to cope with the challenge of substrate inhibition at high‐substrate environments. Finally, the model predictions of optimal microbial growth dynamics are investigated from various inoculum sizes. The proposed modeling approach provides novel insights into the connection between microbial fermentation and substrate supply, facilitating efficient substrate utilization in bioprocess engineering.