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Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines

The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler‐Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines...

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Autores principales: Haringa, Cees, Tang, Wenjun, Deshmukh, Amit T., Xia, Jianye, Reuss, Matthias, Heijnen, Joseph J., Mudde, Robert F., Noorman, Henk J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5129516/
https://www.ncbi.nlm.nih.gov/pubmed/27917102
http://dx.doi.org/10.1002/elsc.201600061
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author Haringa, Cees
Tang, Wenjun
Deshmukh, Amit T.
Xia, Jianye
Reuss, Matthias
Heijnen, Joseph J.
Mudde, Robert F.
Noorman, Henk J.
author_facet Haringa, Cees
Tang, Wenjun
Deshmukh, Amit T.
Xia, Jianye
Reuss, Matthias
Heijnen, Joseph J.
Mudde, Robert F.
Noorman, Henk J.
author_sort Haringa, Cees
collection PubMed
description The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler‐Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines provides deep insight in the dynamic environment inside a large‐scale fermentor, from the point of view of the microorganisms themselves. We present a novel methodology to evaluate this metabolic response, based on transitions between metabolic “regimes” that can provide a comprehensive statistical insight in the environmental fluctuations experienced by microorganisms inside an industrial bioreactor. These statistics provide the groundwork for the design of representative scale‐down simulators, mimicking substrate variations experimentally. To focus on the methodology we use an industrial fermentation of Penicillium chrysogenum in a simplified representation, dealing with only glucose gradients, single‐phase hydrodynamics, and assuming no limitation in oxygen supply, but reasonably capturing the relevant timescales. Nevertheless, the methodology provides useful insight in the relation between flow and component fluctuation timescales that are expected to hold in physically more thorough simulations. Microorganisms experience substrate fluctuations at timescales of seconds, in the order of magnitude of the global circulation time. Such rapid fluctuations should be replicated in truly industrially representative scale‐down simulators.
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spelling pubmed-51295162016-11-30 Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines Haringa, Cees Tang, Wenjun Deshmukh, Amit T. Xia, Jianye Reuss, Matthias Heijnen, Joseph J. Mudde, Robert F. Noorman, Henk J. Eng Life Sci Research Articles The trajectories, referred to as lifelines, of individual microorganisms in an industrial scale fermentor under substrate limiting conditions were studied using an Euler‐Lagrange computational fluid dynamics approach. The metabolic response to substrate concentration variations along these lifelines provides deep insight in the dynamic environment inside a large‐scale fermentor, from the point of view of the microorganisms themselves. We present a novel methodology to evaluate this metabolic response, based on transitions between metabolic “regimes” that can provide a comprehensive statistical insight in the environmental fluctuations experienced by microorganisms inside an industrial bioreactor. These statistics provide the groundwork for the design of representative scale‐down simulators, mimicking substrate variations experimentally. To focus on the methodology we use an industrial fermentation of Penicillium chrysogenum in a simplified representation, dealing with only glucose gradients, single‐phase hydrodynamics, and assuming no limitation in oxygen supply, but reasonably capturing the relevant timescales. Nevertheless, the methodology provides useful insight in the relation between flow and component fluctuation timescales that are expected to hold in physically more thorough simulations. Microorganisms experience substrate fluctuations at timescales of seconds, in the order of magnitude of the global circulation time. Such rapid fluctuations should be replicated in truly industrially representative scale‐down simulators. John Wiley and Sons Inc. 2016-09-14 2016-10 /pmc/articles/PMC5129516/ /pubmed/27917102 http://dx.doi.org/10.1002/elsc.201600061 Text en © 2016 The Authors. Engineering in Life Sciences Published by Wiley‐VCH Verlag GmbH & Co. KGaA This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Haringa, Cees
Tang, Wenjun
Deshmukh, Amit T.
Xia, Jianye
Reuss, Matthias
Heijnen, Joseph J.
Mudde, Robert F.
Noorman, Henk J.
Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines
title Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines
title_full Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines
title_fullStr Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines
title_full_unstemmed Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines
title_short Euler‐Lagrange computational fluid dynamics for (bio)reactor scale down: An analysis of organism lifelines
title_sort euler‐lagrange computational fluid dynamics for (bio)reactor scale down: an analysis of organism lifelines
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5129516/
https://www.ncbi.nlm.nih.gov/pubmed/27917102
http://dx.doi.org/10.1002/elsc.201600061
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