# Mechanistic two‐pathway modeling of substrate inhibition in lactic acid bacteria for enhanced fermentation control

## TL;DR

This paper introduces a new model to understand and control substrate inhibition in lactic acid bacteria during fermentation.

## Contribution

A novel two-pathway model integrating molecular regulation and microbial growth to explain substrate inhibition in lactic acid bacteria.

## Key findings

- The model captures growth dynamics across all phases with one parameter set.
- High substrate concentrations cause prolonged lag phases, as shown by model simulations.
- Preculture treatments can help mitigate substrate inhibition in high-substrate environments.

## Abstract

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.