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Characterizing steady states of genome-scale metabolic networks in continuous cell cultures
In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, ta...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703580/ https://www.ncbi.nlm.nih.gov/pubmed/29131817 http://dx.doi.org/10.1371/journal.pcbi.1005835 |
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author | Fernandez-de-Cossio-Diaz, Jorge Leon, Kalet Mulet, Roberto |
author_facet | Fernandez-de-Cossio-Diaz, Jorge Leon, Kalet Mulet, Roberto |
author_sort | Fernandez-de-Cossio-Diaz, Jorge |
collection | PubMed |
description | In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. |
format | Online Article Text |
id | pubmed-5703580 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57035802017-12-08 Characterizing steady states of genome-scale metabolic networks in continuous cell cultures Fernandez-de-Cossio-Diaz, Jorge Leon, Kalet Mulet, Roberto PLoS Comput Biol Research Article In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. Public Library of Science 2017-11-13 /pmc/articles/PMC5703580/ /pubmed/29131817 http://dx.doi.org/10.1371/journal.pcbi.1005835 Text en © 2017 Fernandez-de-Cossio-Diaz et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fernandez-de-Cossio-Diaz, Jorge Leon, Kalet Mulet, Roberto Characterizing steady states of genome-scale metabolic networks in continuous cell cultures |
title | Characterizing steady states of genome-scale metabolic networks in continuous cell cultures |
title_full | Characterizing steady states of genome-scale metabolic networks in continuous cell cultures |
title_fullStr | Characterizing steady states of genome-scale metabolic networks in continuous cell cultures |
title_full_unstemmed | Characterizing steady states of genome-scale metabolic networks in continuous cell cultures |
title_short | Characterizing steady states of genome-scale metabolic networks in continuous cell cultures |
title_sort | characterizing steady states of genome-scale metabolic networks in continuous cell cultures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703580/ https://www.ncbi.nlm.nih.gov/pubmed/29131817 http://dx.doi.org/10.1371/journal.pcbi.1005835 |
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