A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation
Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process...
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description | Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1‐antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by‐products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α‐ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.
In this study a dynamic mathematical model of cell growth and metabolism was established the for the human cell line AGE1.HN. This model uses ordinary differential equations to simulate changes in viable cell concentration and volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. It accurately estimated the release of metabolic by‐products such as lactate and ammonium directly from the intracellular reactions and the model simulations hints at the existence of distinct cellular physiological states. |
doi_str_mv | 10.1002/bit.27288 |
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In this study a dynamic mathematical model of cell growth and metabolism was established the for the human cell line AGE1.HN. This model uses ordinary differential equations to simulate changes in viable cell concentration and volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. It accurately estimated the release of metabolic by‐products such as lactate and ammonium directly from the intracellular reactions and the model simulations hints at the existence of distinct cellular physiological states.</description><identifier>ISSN: 0006-3592</identifier><identifier>EISSN: 1097-0290</identifier><identifier>DOI: 10.1002/bit.27288</identifier><identifier>PMID: 32022250</identifier><language>eng</language><publisher>HOBOKEN: Wiley</publisher><subject>Adenosine triphosphate ; Ammonium ; animal cell ; ATP ; Biotechnology & Applied Microbiology ; Case studies ; Cell growth ; Cell size ; Citric acid ; Computer simulation ; Culture media ; Design parameters ; Differential equations ; dynamic model ; Dynamic models ; Glycolysis ; Intracellular ; Ketoglutaric acid ; kinetic ; Lactic acid ; Life Sciences & Biomedicine ; Mathematical models ; Metabolism ; Metabolites ; Optimization ; Ordinary differential equations ; Parameter identification ; Physiology ; Process parameters ; Science & Technology ; Substrates ; TCA cycle ; Tricarboxylic acid cycle</subject><ispartof>Biotechnology and bioengineering, 2020-05, Vol.117 (5), p.1533-1553</ispartof><rights>2020 The Authors. published by Wiley Periodicals, Inc.</rights><rights>2020 The Authors. 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C.</creatorcontrib><creatorcontrib>Rath, Alexander G.</creatorcontrib><creatorcontrib>Genzel, Yvonne</creatorcontrib><creatorcontrib>Sandig, Volker</creatorcontrib><creatorcontrib>Reichl, Udo</creatorcontrib><title>A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation</title><title>Biotechnology and bioengineering</title><addtitle>BIOTECHNOL BIOENG</addtitle><addtitle>Biotechnol Bioeng</addtitle><description>Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1‐antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by‐products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α‐ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.
In this study a dynamic mathematical model of cell growth and metabolism was established the for the human cell line AGE1.HN. This model uses ordinary differential equations to simulate changes in viable cell concentration and volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. 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C.</creatorcontrib><creatorcontrib>Rath, Alexander G.</creatorcontrib><creatorcontrib>Genzel, Yvonne</creatorcontrib><creatorcontrib>Sandig, Volker</creatorcontrib><creatorcontrib>Reichl, Udo</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Biotechnology and bioengineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ramos, João R. C.</au><au>Rath, Alexander G.</au><au>Genzel, Yvonne</au><au>Sandig, Volker</au><au>Reichl, Udo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation</atitle><jtitle>Biotechnology and bioengineering</jtitle><stitle>BIOTECHNOL BIOENG</stitle><addtitle>Biotechnol Bioeng</addtitle><date>2020-05</date><risdate>2020</risdate><volume>117</volume><issue>5</issue><spage>1533</spage><epage>1553</epage><pages>1533-1553</pages><issn>0006-3592</issn><eissn>1097-0290</eissn><abstract>Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1‐antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by‐products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α‐ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.
In this study a dynamic mathematical model of cell growth and metabolism was established the for the human cell line AGE1.HN. This model uses ordinary differential equations to simulate changes in viable cell concentration and volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. It accurately estimated the release of metabolic by‐products such as lactate and ammonium directly from the intracellular reactions and the model simulations hints at the existence of distinct cellular physiological states.</abstract><cop>HOBOKEN</cop><pub>Wiley</pub><pmid>32022250</pmid><doi>10.1002/bit.27288</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-6832-6774</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adenosine triphosphate Ammonium animal cell ATP Biotechnology & Applied Microbiology Case studies Cell growth Cell size Citric acid Computer simulation Culture media Design parameters Differential equations dynamic model Dynamic models Glycolysis Intracellular Ketoglutaric acid kinetic Lactic acid Life Sciences & Biomedicine Mathematical models Metabolism Metabolites Optimization Ordinary differential equations Parameter identification Physiology Process parameters Science & Technology Substrates TCA cycle Tricarboxylic acid cycle |
title | A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation |
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