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.

Subjects

Bayesian optimization

Lignocellulose

Saccharification

DDC Class

660.6: Biotechnology

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publication version

publishedVersion

Name

1-s2.0-S2772508126000578-main.pdf

Type

Main Article

Size

1.2 MB

Format

Adobe PDF

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Model-based optimization of enzyme cocktails for sustainable lignocellulosic biomass saccharification