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Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation
Improved understanding of heterogeneous cellulose hydrolysis by cellulases is the basis for optimising enzymatic catalysis-based cellulosic biorefineries. A detailed mechanistic model is developed to describe the dynamic adsorption/desorption and synergistic chain-end scissions of cellulases (endogl...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4705870/ https://www.ncbi.nlm.nih.gov/pubmed/26865832 http://dx.doi.org/10.1016/j.bej.2015.10.017 |
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author | Niu, Hongxing Shah, Nilay Kontoravdi, Cleo |
author_facet | Niu, Hongxing Shah, Nilay Kontoravdi, Cleo |
author_sort | Niu, Hongxing |
collection | PubMed |
description | Improved understanding of heterogeneous cellulose hydrolysis by cellulases is the basis for optimising enzymatic catalysis-based cellulosic biorefineries. A detailed mechanistic model is developed to describe the dynamic adsorption/desorption and synergistic chain-end scissions of cellulases (endoglucanase, exoglucanase, and β-glucosidase) upon amorphous cellulose. The model can predict evolutions of the chain lengths of insoluble cellulose polymers and production of soluble sugars during hydrolysis. Simultaneously, a modelling framework for uncertainty analysis is built based on a quasi-Monte-Carlo method and global sensitivity analysis, which can systematically identify key parameters, help refine the model and improve its identifiability. The model, initially comprising 27 parameters, is found to be over-parameterized with structural and practical identification problems under usual operating conditions (low enzyme loadings). The parameter estimation problem is therefore mathematically ill posed. The framework allows us, on the one hand, to identify a subset of 13 crucial parameters, of which more accurate confidence intervals are estimated using a given experimental dataset, and, on the other hand, to overcome the identification problems. The model’s predictive capability is checked against an independent set of experimental data. Finally, the optimal composition of cellulases cocktail is obtained by model-based optimisation both for enzymatic hydrolysis and for the process of simultaneous saccharification and fermentation. |
format | Online Article Text |
id | pubmed-4705870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-47058702016-02-08 Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation Niu, Hongxing Shah, Nilay Kontoravdi, Cleo Biochem Eng J Regular Article Improved understanding of heterogeneous cellulose hydrolysis by cellulases is the basis for optimising enzymatic catalysis-based cellulosic biorefineries. A detailed mechanistic model is developed to describe the dynamic adsorption/desorption and synergistic chain-end scissions of cellulases (endoglucanase, exoglucanase, and β-glucosidase) upon amorphous cellulose. The model can predict evolutions of the chain lengths of insoluble cellulose polymers and production of soluble sugars during hydrolysis. Simultaneously, a modelling framework for uncertainty analysis is built based on a quasi-Monte-Carlo method and global sensitivity analysis, which can systematically identify key parameters, help refine the model and improve its identifiability. The model, initially comprising 27 parameters, is found to be over-parameterized with structural and practical identification problems under usual operating conditions (low enzyme loadings). The parameter estimation problem is therefore mathematically ill posed. The framework allows us, on the one hand, to identify a subset of 13 crucial parameters, of which more accurate confidence intervals are estimated using a given experimental dataset, and, on the other hand, to overcome the identification problems. The model’s predictive capability is checked against an independent set of experimental data. Finally, the optimal composition of cellulases cocktail is obtained by model-based optimisation both for enzymatic hydrolysis and for the process of simultaneous saccharification and fermentation. Elsevier 2016-01-15 /pmc/articles/PMC4705870/ /pubmed/26865832 http://dx.doi.org/10.1016/j.bej.2015.10.017 Text en © 2015 The Authors http://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 | Regular Article Niu, Hongxing Shah, Nilay Kontoravdi, Cleo Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation |
title | Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation |
title_full | Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation |
title_fullStr | Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation |
title_full_unstemmed | Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation |
title_short | Modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation |
title_sort | modelling of amorphous cellulose depolymerisation by cellulases, parametric studies and optimisation |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4705870/ https://www.ncbi.nlm.nih.gov/pubmed/26865832 http://dx.doi.org/10.1016/j.bej.2015.10.017 |
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