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Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture

Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis–Menten equation. In the cas...

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Autor principal: Xu, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027892/
https://www.ncbi.nlm.nih.gov/pubmed/31758551
http://dx.doi.org/10.1002/bit.27230
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author Xu, Peng
author_facet Xu, Peng
author_sort Xu, Peng
collection PubMed
description Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis–Menten equation. In the case of the Logistic equation, the specific growth rate is determined by the carrying capacity of the system, which could be growth‐inhibiting factors (i.e., toxic chemical accumulation) other than the nutrient level. Both equations have been found valuable to guide us build unstructured kinetic models to analyze the fermentation process and understand cell physiology. In this work, we present a hybrid Logistic‐Monod growth model, which accounts for multiple growth‐dependent factors including both the limiting nutrient and the carrying capacity of the system. Coupled with substrate consumption and yield coefficient, we present the analytical solutions for this hybrid Logistic‐Monod model in both batch and continuous stirred tank reactor (CSTR) culture. Under high biomass yield (Y (x/s)) conditions, the analytical solution for this hybrid model is approaching to the Logistic equation; under low biomass yield condition, the analytical solution for this hybrid model converges to the Monod equation. This hybrid Logistic‐Monod equation represents the cell growth transition from substrate‐limiting condition to growth‐inhibiting condition, which could be adopted to accurately describe the multi‐phases of cell growth and may facilitate kinetic model construction, bioprocess optimization, and scale‐up in industrial biotechnology.
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spelling pubmed-70278922020-02-24 Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture Xu, Peng Biotechnol Bioeng COMMUNICATION TO THE EDITOR Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis–Menten equation. In the case of the Logistic equation, the specific growth rate is determined by the carrying capacity of the system, which could be growth‐inhibiting factors (i.e., toxic chemical accumulation) other than the nutrient level. Both equations have been found valuable to guide us build unstructured kinetic models to analyze the fermentation process and understand cell physiology. In this work, we present a hybrid Logistic‐Monod growth model, which accounts for multiple growth‐dependent factors including both the limiting nutrient and the carrying capacity of the system. Coupled with substrate consumption and yield coefficient, we present the analytical solutions for this hybrid Logistic‐Monod model in both batch and continuous stirred tank reactor (CSTR) culture. Under high biomass yield (Y (x/s)) conditions, the analytical solution for this hybrid model is approaching to the Logistic equation; under low biomass yield condition, the analytical solution for this hybrid model converges to the Monod equation. This hybrid Logistic‐Monod equation represents the cell growth transition from substrate‐limiting condition to growth‐inhibiting condition, which could be adopted to accurately describe the multi‐phases of cell growth and may facilitate kinetic model construction, bioprocess optimization, and scale‐up in industrial biotechnology. John Wiley and Sons Inc. 2019-12-02 2020-03 /pmc/articles/PMC7027892/ /pubmed/31758551 http://dx.doi.org/10.1002/bit.27230 Text en © 2019 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc. This is an open access article under the terms of the 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 COMMUNICATION TO THE EDITOR
Xu, Peng
Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture
title Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture
title_full Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture
title_fullStr Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture
title_full_unstemmed Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture
title_short Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture
title_sort analytical solution for a hybrid logistic‐monod cell growth model in batch and continuous stirred tank reactor culture
topic COMMUNICATION TO THE EDITOR
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027892/
https://www.ncbi.nlm.nih.gov/pubmed/31758551
http://dx.doi.org/10.1002/bit.27230
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