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An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling
BACKGROUND: Aspergillus niger fermentation has provided the chief source of industrial citric acid for over 50 years. Traditional strain development of this organism was achieved through random mutagenesis, but advances in genomics have enabled the development of genome-scale metabolic modelling tha...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679502/ https://www.ncbi.nlm.nih.gov/pubmed/29151887 http://dx.doi.org/10.1186/s13068-017-0950-6 |
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author | Upton, Daniel J. McQueen-Mason, Simon J. Wood, A. Jamie |
author_facet | Upton, Daniel J. McQueen-Mason, Simon J. Wood, A. Jamie |
author_sort | Upton, Daniel J. |
collection | PubMed |
description | BACKGROUND: Aspergillus niger fermentation has provided the chief source of industrial citric acid for over 50 years. Traditional strain development of this organism was achieved through random mutagenesis, but advances in genomics have enabled the development of genome-scale metabolic modelling that can be used to make predictive improvements in fermentation performance. The parent citric acid-producing strain of A. niger, ATCC 1015, has been described previously by a genome-scale metabolic model that encapsulates its response to ambient pH. Here, we report the development of a novel double optimisation modelling approach that generates time-dependent citric acid fermentation using dynamic flux balance analysis. RESULTS: The output from this model shows a good match with empirical fermentation data. Our studies suggest that citric acid production commences upon a switch to phosphate-limited growth and this is validated by fitting to empirical data, which confirms the diauxic growth behaviour and the role of phosphate storage as polyphosphate. CONCLUSIONS: The calibrated time-course model reflects observed metabolic events and generates reliable in silico data for industrially relevant fermentative time series, and for the behaviour of engineered strains suggesting that our approach can be used as a powerful tool for predictive metabolic engineering. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13068-017-0950-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5679502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56795022017-11-17 An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling Upton, Daniel J. McQueen-Mason, Simon J. Wood, A. Jamie Biotechnol Biofuels Research BACKGROUND: Aspergillus niger fermentation has provided the chief source of industrial citric acid for over 50 years. Traditional strain development of this organism was achieved through random mutagenesis, but advances in genomics have enabled the development of genome-scale metabolic modelling that can be used to make predictive improvements in fermentation performance. The parent citric acid-producing strain of A. niger, ATCC 1015, has been described previously by a genome-scale metabolic model that encapsulates its response to ambient pH. Here, we report the development of a novel double optimisation modelling approach that generates time-dependent citric acid fermentation using dynamic flux balance analysis. RESULTS: The output from this model shows a good match with empirical fermentation data. Our studies suggest that citric acid production commences upon a switch to phosphate-limited growth and this is validated by fitting to empirical data, which confirms the diauxic growth behaviour and the role of phosphate storage as polyphosphate. CONCLUSIONS: The calibrated time-course model reflects observed metabolic events and generates reliable in silico data for industrially relevant fermentative time series, and for the behaviour of engineered strains suggesting that our approach can be used as a powerful tool for predictive metabolic engineering. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13068-017-0950-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-09 /pmc/articles/PMC5679502/ /pubmed/29151887 http://dx.doi.org/10.1186/s13068-017-0950-6 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Upton, Daniel J. McQueen-Mason, Simon J. Wood, A. Jamie An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling |
title | An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling |
title_full | An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling |
title_fullStr | An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling |
title_full_unstemmed | An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling |
title_short | An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling |
title_sort | accurate description of aspergillus niger organic acid batch fermentation through dynamic metabolic modelling |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679502/ https://www.ncbi.nlm.nih.gov/pubmed/29151887 http://dx.doi.org/10.1186/s13068-017-0950-6 |
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