Model-Based Optimization of Enzyme Cocktails for Sustainable Lignocellulosic Biomass Saccharification

Lignocellulosic biomass has a high potential as resource for the chemical industry, but its conversion to fermentable sugars requires efficient pre-treatment and a subsequent enzymatic saccharification. Here, we present a model-based workflow for the design of enzyme cocktails for lignocellulosic biomass saccharification. Saccharification performance depends on several factors, such as substrate structure and pre-treatment severity, enzyme synergy, and inhibition effects. Because of this complexity, optimization by experimental trial-and-error is expensive and time-consuming, and enzyme mixtures optimized for one substrate often lack transferability to other feedstocks. To address these challenges, this work couples a stochastic simulator of lignocellulose hydrolysis with multi-objective optimization, explicitly resolving the trade-off between conversion speed and enzyme usage as a proxy for costs. The results demonstrate the feasibility and potential of model-based optimisation for rational enzyme cocktail design.