Substrate inhibition is a longstanding bottleneck in microbial fermentation, particularly in lactic acid bacteria, where excessive substrate concentrations can paradoxically suppress growth and product formation. Conventional models, such as Haldane type formulations, are widely used but remain largely phenomenological, limiting their interpretability and predictive power for process optimization. Bridging this gap requires models that not only fit experimental data but also capture the underlying biological mechanisms.

Recently, a biophysical research group at Huzhou University published an article titled “Mechanistic two-pathway modeling of substrate inhibition in lactic acid bacteria for enhanced fermentation control” in Quantitative Biology. In this work, the authors introduce a mechanistically structured two-pathway model that decomposes substrate effects into a promotive pathway (driving growth and metabolism) and an inhibitory pathway (representing metabolic burden or toxicity at high concentrations). This formulation enables a transparent and biologically interpretable description of substrate inhibition dynamics.

The proposed framework moves beyond traditional single-function inhibition models by explicitly separating competing effects of substrate concentration. Figure 1 illustrates two pathway modeling substrate inhibition kinetics in lactic acid bacteria fermentation. Through systematic parameterization and validation against experimental fermentation data, the model demonstrates strong predictive capability across a wide concentration range, particularly in capturing the transition from growth-dominated to inhibition-dominated regimes. Importantly, key model parameters are directly linked to biologically meaningful quantities, offering practical insights into substrate uptake efficiency and inhibition strength.

This study provides a quantitative and interpretable modeling framework for understanding and controlling substrate inhibition in microbial systems. The model predictions are validated by the published experimental data of LAB batch fermentation such as L. bulgaricus, L. casei, and L. plantarum on lactose, demonstrating its universality beyond specific substrate‐strain systems. By enabling more accurate prediction of optimal operating conditions, the model has clear implications for improving fermentation efficiency and guiding feeding strategies in industrial biotechnology. More broadly, the two-pathway approach offers a generalizable paradigm for mechanistic modeling of nonlinear effects in biological systems.

DOI
10.1002/qub2.70019