Taming complexity in enzymatic saccharification: A predictive hierarchical modeling framework

Lignocellulosic biomass biotechnological conversion relies on enzymatic hydrolysis of recalcitrant plant cell walls, which limits conversion efficiency and economic viability and renders sugar-release dynamics difficult to predict. Mathematical modeling has therefore emerged as a key approach for elucidating the mechanisms governing enzymatic hydrolysis and predicting its dynamics. Here, a hierarchical adsorption-inhibition framework is developed, beginning with a detailed Dynamic Adsorption-Inhibition Model (DyAIM). This model is then simplified into a Reduced Adsorption-Inhibition Model (ReAIM) by pruning weak adsorption and inhibition interactions. Finally, ReAIM is further simplified into an Effective Activity Model (EAM), in which nonproductive enzyme adsorption onto lignin is represented by a reduction in effective enzymatic activity. Plant cell wall composition is expressed as four structural polymers coupled to six soluble products, with inhibition by mono- and oligosaccharides acting on five functional enzyme pools. Using glucose, xylose and mannose release at two enzyme loadings, the model hierarchy is validated in reproducing saccharification dynamics and in delivering a quantitative product-enzyme inhibition network consistent with literature trends. The model framework was further validated by prediction of saccharification dynamics at an unseen enzyme loading, demonstrating robust performance beyond the calibration conditions. Sensitivity analysis further supported the progressive simplifications adopted in ReAIM and EAM. Maintaining predictive performance across increasing levels of simplification indicates that the hierarchy effectively distinguishes essential mechanisms from dispensable complexity, enabling rational model selection.