Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms

Autores
Luna, Martín Francisco; Mione, Federico Martin; Kaspersetz, Lucas; Neubauer, Peter; Martínez, Ernesto Carlos; Cruz Bournazou, M. Nicolas
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Mathematical models of bioreactors are powerful tools that aid in the analysis and prediction of process operation. However, the complex behavior of microorganisms makes modelling of biological processes a particularly challenging task, especially in the early developmental stages when data and knowledge are scarce. As a result, bioreactor models may perform poorly due to structural errors or high uncertainty in their parameterization. Here, we present a method for automated dynamic model regression based on a Bayesian approach that can be applied in the operation of laboratory robotic platforms to perform both parameter estimation and state predictions for a given experimental design. Starting with wide distributions over parameters (prior knowledge), the model is updated as new data is generated and is then used to predict the evolution of the experiment. The proposed method is tested with data from several parallel cultivations from a 24 mini-bioreactors platform containing an Escherichia coli strain operating in fed-batch mode. The results highlight both the versatility of the approach to estimate parameter distribution as well as to predict the state evolution.
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: Mione, Federico Martin. 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: Kaspersetz, Lucas. Technishe Universitat Berlin; Alemania
Fil: Neubauer, Peter. Technishe Universitat Berlin; Alemania
Fil: Martínez, 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
Fil: Cruz Bournazou, M. Nicolas. Technishe Universitat Berlin; Alemania
Materia
Variational Bayesian inference
Bioprocess modelling
Robotic platform
Laboratory Automation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/273516
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platformsLuna, Martín FranciscoMione, Federico MartinKaspersetz, LucasNeubauer, PeterMartínez, Ernesto CarlosCruz Bournazou, M. NicolasVariational Bayesian inferenceBioprocess modellingRobotic platformLaboratory Automationhttps://purl.org/becyt/ford/2.9https://purl.org/becyt/ford/2Mathematical models of bioreactors are powerful tools that aid in the analysis and prediction of process operation. However, the complex behavior of microorganisms makes modelling of biological processes a particularly challenging task, especially in the early developmental stages when data and knowledge are scarce. As a result, bioreactor models may perform poorly due to structural errors or high uncertainty in their parameterization. Here, we present a method for automated dynamic model regression based on a Bayesian approach that can be applied in the operation of laboratory robotic platforms to perform both parameter estimation and state predictions for a given experimental design. Starting with wide distributions over parameters (prior knowledge), the model is updated as new data is generated and is then used to predict the evolution of the experiment. The proposed method is tested with data from several parallel cultivations from a 24 mini-bioreactors platform containing an Escherichia coli strain operating in fed-batch mode. The results highlight both the versatility of the approach to estimate parameter distribution as well as to predict the state evolution.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: Mione, Federico Martin. 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: Kaspersetz, Lucas. Technishe Universitat Berlin; AlemaniaFil: Neubauer, Peter. Technishe Universitat Berlin; AlemaniaFil: Martínez, 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; ArgentinaFil: Cruz Bournazou, M. Nicolas. Technishe Universitat Berlin; AlemaniaElsevier Science SA2025-03info: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/11336/273516Luna, Martín Francisco; Mione, Federico Martin; Kaspersetz, Lucas; Neubauer, Peter; Martínez, Ernesto Carlos; et al.; Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms; Elsevier Science SA; Biochemical Engineering Journal; 219; 3-2025; 1-141369-703XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1369703X25001032info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bej.2025.109729info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-05T09:44:21Zoai:ri.conicet.gov.ar:11336/273516instacron: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:34982025-11-05 09:44:22.155CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
title Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
spellingShingle Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
Luna, Martín Francisco
Variational Bayesian inference
Bioprocess modelling
Robotic platform
Laboratory Automation
title_short Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
title_full Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
title_fullStr Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
title_full_unstemmed Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
title_sort Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms
dc.creator.none.fl_str_mv Luna, Martín Francisco
Mione, Federico Martin
Kaspersetz, Lucas
Neubauer, Peter
Martínez, Ernesto Carlos
Cruz Bournazou, M. Nicolas
author Luna, Martín Francisco
author_facet Luna, Martín Francisco
Mione, Federico Martin
Kaspersetz, Lucas
Neubauer, Peter
Martínez, Ernesto Carlos
Cruz Bournazou, M. Nicolas
author_role author
author2 Mione, Federico Martin
Kaspersetz, Lucas
Neubauer, Peter
Martínez, Ernesto Carlos
Cruz Bournazou, M. Nicolas
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Variational Bayesian inference
Bioprocess modelling
Robotic platform
Laboratory Automation
topic Variational Bayesian inference
Bioprocess modelling
Robotic platform
Laboratory Automation
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.9
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Mathematical models of bioreactors are powerful tools that aid in the analysis and prediction of process operation. However, the complex behavior of microorganisms makes modelling of biological processes a particularly challenging task, especially in the early developmental stages when data and knowledge are scarce. As a result, bioreactor models may perform poorly due to structural errors or high uncertainty in their parameterization. Here, we present a method for automated dynamic model regression based on a Bayesian approach that can be applied in the operation of laboratory robotic platforms to perform both parameter estimation and state predictions for a given experimental design. Starting with wide distributions over parameters (prior knowledge), the model is updated as new data is generated and is then used to predict the evolution of the experiment. The proposed method is tested with data from several parallel cultivations from a 24 mini-bioreactors platform containing an Escherichia coli strain operating in fed-batch mode. The results highlight both the versatility of the approach to estimate parameter distribution as well as to predict the state evolution.
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: Mione, Federico Martin. 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: Kaspersetz, Lucas. Technishe Universitat Berlin; Alemania
Fil: Neubauer, Peter. Technishe Universitat Berlin; Alemania
Fil: Martínez, 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
Fil: Cruz Bournazou, M. Nicolas. Technishe Universitat Berlin; Alemania
description Mathematical models of bioreactors are powerful tools that aid in the analysis and prediction of process operation. However, the complex behavior of microorganisms makes modelling of biological processes a particularly challenging task, especially in the early developmental stages when data and knowledge are scarce. As a result, bioreactor models may perform poorly due to structural errors or high uncertainty in their parameterization. Here, we present a method for automated dynamic model regression based on a Bayesian approach that can be applied in the operation of laboratory robotic platforms to perform both parameter estimation and state predictions for a given experimental design. Starting with wide distributions over parameters (prior knowledge), the model is updated as new data is generated and is then used to predict the evolution of the experiment. The proposed method is tested with data from several parallel cultivations from a 24 mini-bioreactors platform containing an Escherichia coli strain operating in fed-batch mode. The results highlight both the versatility of the approach to estimate parameter distribution as well as to predict the state evolution.
publishDate 2025
dc.date.none.fl_str_mv 2025-03
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/11336/273516
Luna, Martín Francisco; Mione, Federico Martin; Kaspersetz, Lucas; Neubauer, Peter; Martínez, Ernesto Carlos; et al.; Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms; Elsevier Science SA; Biochemical Engineering Journal; 219; 3-2025; 1-14
1369-703X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/273516
identifier_str_mv Luna, Martín Francisco; Mione, Federico Martin; Kaspersetz, Lucas; Neubauer, Peter; Martínez, Ernesto Carlos; et al.; Automated regression of bioreactor models using a Bayesian approach for parallel cultivations in robotic platforms; Elsevier Science SA; Biochemical Engineering Journal; 219; 3-2025; 1-14
1369-703X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1369703X25001032
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bej.2025.109729
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science SA
publisher.none.fl_str_mv Elsevier Science SA
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 13.084122

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