Summary
Enzymatic conversions of lignocellulosic biomass are important operations in biorefinery approaches. However, they are difficult to optimise due to the variability and heterogeneity of the substrate. Especially
predicting lignocellulose saccharification would be crucial for lignocellulosic biorefinery concepts to reach economic figures. This necessitates advanced computational tools, and deep understanding of the underlying microscopic processes. PREDIG builds on previous computational work of the team and aims to deliver a free, open-source, modular and user-friendly software to predict saccharification dynamics for different types of biomass. To calibrate, test and empower this unique predictive tool, the PREDIG approach will utilise existing materials and data sets. Quantitative experiments will be performed to complement these data. In particular, investigating the impact of structural features will be focussed in sets of plant material with large diversity in composition, and in saccharification properties. Importantly, the project will not only yield a tool to answer precise applied questions (for instance best adapted enzyme cocktails), but it will also provide new fundamental knowledge on the microscopic mechanisms and kinetics of lignocellulose saccharification.
Results
As overall goal of the BioSC Seed Fund 3.0 project PREDIG, the team led by Dr Adélaïde Raguin (HHU Düsseldorf) has successfully developed a Web Application to simulate biomass saccharification, that is now online available and published in the Computational and Structural Biotechnology Journal.
Dr. Raguin
Computional Cell Biology
HHU Düsseldorf
email: Adelaide.Raguin[at]hhu.de
Prof. Dr. Lercher, Computional Cell Biology, HHU Düsseldorf
Prof. Dr. Schurr & Dr. Klose & Dr. Grande, IBG-2: Plant Sciences, Forschungszentrum Jülich
01.02.2022 - 30.09.2023
PREDIG is part of the NRW-Strategieprojekt BioSC and thus funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia.
De, PS, Theilmann, J and Raguin, A (2024). A detailed sensitivity analysis identifies the key factors influencing the enzymatic saccharification of lignocellulosic biomass. Computational and Structural Biotechnology Journal 23: 1005-1015.
De, PS, Glass, T, Stein, M, Spitzlei, T and Raguin, A (2023). PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall. Computational and Structural Biotechnology Journal 21: 5463-5475.
van den Bogaard, S, Saa, PA and Alter, TB (2024). Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control. Bioinformatics 40(12): btae691.
Klose, H and Paës, G (2023). Editorial: Understanding plant cell wall recalcitrance for efficient lignocellulose processing. Frontiers in Plant Science 14.
Martinez Diaz, J, Grande, PM and Klose, H (2023). Small-scale OrganoCat processing to screen rapeseed straw for efficient lignocellulose fractionation. Frontiers in Chemical Engineering 5.