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PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall
Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and pro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663758/ https://www.ncbi.nlm.nih.gov/pubmed/38022701 http://dx.doi.org/10.1016/j.csbj.2023.09.026 |
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author | De, Partho Sakha Glass, Torben Stein, Merle Spitzlei, Thomas Raguin, Adélaïde |
author_facet | De, Partho Sakha Glass, Torben Stein, Merle Spitzlei, Thomas Raguin, Adélaïde |
author_sort | De, Partho Sakha |
collection | PubMed |
description | Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and profitability of biofuel production. To complement both academic and industrial experimental research in the field, we designed an advanced web application that encapsulates our in-house developed complex biophysical model of enzymatic plant cell wall degradation. PREDIG (https://predig.cs.hhu.de/) is a user-friendly, free, and fully open-source web application that allows the user to perform in silico experiments. Specifically, it uses a Gillespie algorithm to run stochastic simulations of the enzymatic saccharification of a lignocellulose microfibril, at the mesoscale, in three dimensions. Such simulations can for instance be used to test the action of distinct enzyme cocktails on the substrate. Additionally, PREDIG can fit the model parameters to uploaded experimental time-course data, thereby returning values that are intrinsically difficult to measure experimentally. This gives the user the possibility to learn which factors quantitatively explain the recalcitrance to saccharification of their specific biomass material. |
format | Online Article Text |
id | pubmed-10663758 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-106637582023-09-29 PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall De, Partho Sakha Glass, Torben Stein, Merle Spitzlei, Thomas Raguin, Adélaïde Comput Struct Biotechnol J Software/Web server Article Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and profitability of biofuel production. To complement both academic and industrial experimental research in the field, we designed an advanced web application that encapsulates our in-house developed complex biophysical model of enzymatic plant cell wall degradation. PREDIG (https://predig.cs.hhu.de/) is a user-friendly, free, and fully open-source web application that allows the user to perform in silico experiments. Specifically, it uses a Gillespie algorithm to run stochastic simulations of the enzymatic saccharification of a lignocellulose microfibril, at the mesoscale, in three dimensions. Such simulations can for instance be used to test the action of distinct enzyme cocktails on the substrate. Additionally, PREDIG can fit the model parameters to uploaded experimental time-course data, thereby returning values that are intrinsically difficult to measure experimentally. This gives the user the possibility to learn which factors quantitatively explain the recalcitrance to saccharification of their specific biomass material. Research Network of Computational and Structural Biotechnology 2023-09-29 /pmc/articles/PMC10663758/ /pubmed/38022701 http://dx.doi.org/10.1016/j.csbj.2023.09.026 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Software/Web server Article De, Partho Sakha Glass, Torben Stein, Merle Spitzlei, Thomas Raguin, Adélaïde PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall |
title | PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall |
title_full | PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall |
title_fullStr | PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall |
title_full_unstemmed | PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall |
title_short | PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall |
title_sort | predig: web application to model and predict the enzymatic saccharification of plant cell wall |
topic | Software/Web server Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663758/ https://www.ncbi.nlm.nih.gov/pubmed/38022701 http://dx.doi.org/10.1016/j.csbj.2023.09.026 |
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