Taming complexity in enzymatic saccharification: A predictive hierarchical modeling framework

The conversion of lignocellulosic biomass into useful sugars faces significant challenges due to the complex structure of plant cell walls, which are resistant to enzymatic hydrolysis. This limits the efficiency and economic viability of the process, making it difficult to predict sugar-release dynamics. Mathematical modeling has become an essential tool in understanding the mechanisms behind enzymatic hydrolysis and predicting its outcomes.

To address these challenges, a hierarchical adsorption-inhibition framework has been developed. This framework begins with a detailed Dynamic Adsorption-Inhibition Model (DyAIM), which is then simplified into a Reduced Adsorption-Inhibition Model (ReAIM) by eliminating weak adsorption and inhibition interactions. The ReAIM is further simplified into an Effective Activity Model (EAM), where nonproductive enzyme adsorption onto lignin is represented by a reduction in effective enzymatic activity.

The plant cell wall is modeled as four structural polymers coupled to six soluble products. Inhibition of the five functional enzyme pools is caused by mono- and oligosaccharides. The validity of the model hierarchy is confirmed by reproducing saccharification dynamics using glucose, xylose, and mannose release at two enzyme loadings. This validation also provides a quantitative product-enzyme inhibition network that aligns with trends found in the literature.

The model framework was further tested by predicting saccharification dynamics at an unseen enzyme loading, showcasing its robust performance beyond the conditions used for calibration. Sensitivity analysis supports the progressive simplifications adopted in ReAIM and EAM, demonstrating that the hierarchy effectively distinguishes essential mechanisms from unnecessary complexity. This enables researchers to make rational choices when selecting models for their specific needs.