A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models

Autores
Luna, Martín Francisco; Martinez, Ernesto Carlos
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Increasing demand for recombinant proteins (including monoclonal antibodies) where time to market is critical could benefit from the use of model-based optimization of cell viability and productivity. Owing to the complexity of metabolic regulation, unstructured models of animal cell cultures typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A Bayesian optimization strategy which integrates tendency models with iterative policy learning is proposed. Parameter distributions in a probabilistic model of bioreactor performance are re-estimated using data from experiments designed for maximizing information content and productivity. Results obtained highlight that experimental design for run-to-run optimization using a probabilistic tendency model is effective to maximize biomass growth even though significant model uncertainty is present. A hybrid cybernetic model of a myeloma cell culture coconsuming glucose and glutamine is used to simulate data to demonstrate the efficacy of the proposed approach.
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
Run-To-Run Optimization
Bioprocess
Tendency Models
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/22438
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency ModelsLuna, Martín FranciscoMartinez, Ernesto CarlosRun-To-Run OptimizationBioprocessTendency Modelshttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Increasing demand for recombinant proteins (including monoclonal antibodies) where time to market is critical could benefit from the use of model-based optimization of cell viability and productivity. Owing to the complexity of metabolic regulation, unstructured models of animal cell cultures typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A Bayesian optimization strategy which integrates tendency models with iterative policy learning is proposed. Parameter distributions in a probabilistic model of bioreactor performance are re-estimated using data from experiments designed for maximizing information content and productivity. Results obtained highlight that experimental design for run-to-run optimization using a probabilistic tendency model is effective to maximize biomass growth even though significant model uncertainty is present. A hybrid cybernetic model of a myeloma cell culture coconsuming glucose and glutamine is used to simulate data to demonstrate the efficacy of the proposed approach.Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaAmerican Chemical Society2014-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/22438Luna, Martín Francisco; Martinez, Ernesto Carlos; A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models; American Chemical Society; Industrial & Engineering Chemical Research; 53; 44; 8-2014; 17252-172660888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie500453einfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie500453einfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:11:35Zoai:ri.conicet.gov.ar:11336/22438instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:11:35.548CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
title A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
spellingShingle A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
Luna, Martín Francisco
Run-To-Run Optimization
Bioprocess
Tendency Models
title_short A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
title_full A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
title_fullStr A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
title_full_unstemmed A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
title_sort A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models
dc.creator.none.fl_str_mv Luna, Martín Francisco
Martinez, Ernesto Carlos
author Luna, Martín Francisco
author_facet Luna, Martín Francisco
Martinez, Ernesto Carlos
author_role author
author2 Martinez, Ernesto Carlos
author2_role author
dc.subject.none.fl_str_mv Run-To-Run Optimization
Bioprocess
Tendency Models
topic Run-To-Run Optimization
Bioprocess
Tendency Models
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Increasing demand for recombinant proteins (including monoclonal antibodies) where time to market is critical could benefit from the use of model-based optimization of cell viability and productivity. Owing to the complexity of metabolic regulation, unstructured models of animal cell cultures typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A Bayesian optimization strategy which integrates tendency models with iterative policy learning is proposed. Parameter distributions in a probabilistic model of bioreactor performance are re-estimated using data from experiments designed for maximizing information content and productivity. Results obtained highlight that experimental design for run-to-run optimization using a probabilistic tendency model is effective to maximize biomass growth even though significant model uncertainty is present. A hybrid cybernetic model of a myeloma cell culture coconsuming glucose and glutamine is used to simulate data to demonstrate the efficacy of the proposed approach.
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description Increasing demand for recombinant proteins (including monoclonal antibodies) where time to market is critical could benefit from the use of model-based optimization of cell viability and productivity. Owing to the complexity of metabolic regulation, unstructured models of animal cell cultures typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A Bayesian optimization strategy which integrates tendency models with iterative policy learning is proposed. Parameter distributions in a probabilistic model of bioreactor performance are re-estimated using data from experiments designed for maximizing information content and productivity. Results obtained highlight that experimental design for run-to-run optimization using a probabilistic tendency model is effective to maximize biomass growth even though significant model uncertainty is present. A hybrid cybernetic model of a myeloma cell culture coconsuming glucose and glutamine is used to simulate data to demonstrate the efficacy of the proposed approach.
publishDate 2014
dc.date.none.fl_str_mv 2014-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/22438
Luna, Martín Francisco; Martinez, Ernesto Carlos; A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models; American Chemical Society; Industrial & Engineering Chemical Research; 53; 44; 8-2014; 17252-17266
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/22438
identifier_str_mv Luna, Martín Francisco; Martinez, Ernesto Carlos; A Bayesian Approach to Run-to-Run Optimization of Animal Cell Bioreactors Using Probabilistic Tendency Models; American Chemical Society; Industrial & Engineering Chemical Research; 53; 44; 8-2014; 17252-17266
0888-5885
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1021/ie500453e
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie500453e
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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