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Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production
As breakthrough cellular therapy discoveries are translated into reliable, commercializable applications, effective stem cell biomanufacturing requires systematically developing and optimizing bioprocess design and operation. This article proposes a rigorous computational framework for stem cell bio...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6108044/ https://www.ncbi.nlm.nih.gov/pubmed/30166646 http://dx.doi.org/10.1002/aic.16042 |
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author | Misener, Ruth Allenby, Mark C. Fuentes‐Garí, María Gupta, Karan Wiggins, Thomas Panoskaltsis, Nicki Pistikopoulos, Efstratios N. Mantalaris, Athanasios |
author_facet | Misener, Ruth Allenby, Mark C. Fuentes‐Garí, María Gupta, Karan Wiggins, Thomas Panoskaltsis, Nicki Pistikopoulos, Efstratios N. Mantalaris, Athanasios |
author_sort | Misener, Ruth |
collection | PubMed |
description | As breakthrough cellular therapy discoveries are translated into reliable, commercializable applications, effective stem cell biomanufacturing requires systematically developing and optimizing bioprocess design and operation. This article proposes a rigorous computational framework for stem cell biomanufacturing under uncertainty. Our mathematical tool kit incorporates: high‐fidelity modeling, single variate and multivariate sensitivity analysis, global topological superstructure optimization, and robust optimization. The advantages of the proposed bioprocess optimization framework using, as a case study, a dual hollow fiber bioreactor producing red blood cells from progenitor cells were quantitatively demonstrated. The optimization phase reduces the cost by a factor of 4, and the price of insuring process performance against uncertainty is approximately 15% over the nominal optimal solution. Mathematical modeling and optimization can guide decision making; the possible commercial impact of this cellular therapy using the disruptive technology paradigm was quantitatively evaluated. © 2017 American Institute of Chemical Engineers AIChE J, 64: 3011–3022, 2018 |
format | Online Article Text |
id | pubmed-6108044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61080442018-08-28 Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production Misener, Ruth Allenby, Mark C. Fuentes‐Garí, María Gupta, Karan Wiggins, Thomas Panoskaltsis, Nicki Pistikopoulos, Efstratios N. Mantalaris, Athanasios AIChE J Futures Issue: Process Systems Engineering As breakthrough cellular therapy discoveries are translated into reliable, commercializable applications, effective stem cell biomanufacturing requires systematically developing and optimizing bioprocess design and operation. This article proposes a rigorous computational framework for stem cell biomanufacturing under uncertainty. Our mathematical tool kit incorporates: high‐fidelity modeling, single variate and multivariate sensitivity analysis, global topological superstructure optimization, and robust optimization. The advantages of the proposed bioprocess optimization framework using, as a case study, a dual hollow fiber bioreactor producing red blood cells from progenitor cells were quantitatively demonstrated. The optimization phase reduces the cost by a factor of 4, and the price of insuring process performance against uncertainty is approximately 15% over the nominal optimal solution. Mathematical modeling and optimization can guide decision making; the possible commercial impact of this cellular therapy using the disruptive technology paradigm was quantitatively evaluated. © 2017 American Institute of Chemical Engineers AIChE J, 64: 3011–3022, 2018 John Wiley and Sons Inc. 2017-12-07 2018-08 /pmc/articles/PMC6108044/ /pubmed/30166646 http://dx.doi.org/10.1002/aic.16042 Text en © 2017 American Institute of Chemical Engineers 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 | Futures Issue: Process Systems Engineering Misener, Ruth Allenby, Mark C. Fuentes‐Garí, María Gupta, Karan Wiggins, Thomas Panoskaltsis, Nicki Pistikopoulos, Efstratios N. Mantalaris, Athanasios Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production |
title | Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production |
title_full | Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production |
title_fullStr | Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production |
title_full_unstemmed | Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production |
title_short | Stem cell biomanufacturing under uncertainty: A case study in optimizing red blood cell production |
title_sort | stem cell biomanufacturing under uncertainty: a case study in optimizing red blood cell production |
topic | Futures Issue: Process Systems Engineering |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6108044/ https://www.ncbi.nlm.nih.gov/pubmed/30166646 http://dx.doi.org/10.1002/aic.16042 |
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