Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques

Autores
Di Maggio, Jimena Andrea; Paulo, Cecilia Inés; Estrada, Vanina Gisela; Perotti, Nora Ines; Diaz Ricci, Juan Carlos; Díaz, María Soledad
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the present work, we formulate parameter estimation problems for kinetic models of large-scale dynamic biotechnological systems. We propose dynamic models of increasing complexity for metabolic networks and continuous bioreactors. The differential algebraic equations (DAE) system for the metabolic network represent the glycolysis, the phosphotransferase system and the pentose-phosphate pathway of Escherichia coli, with modifications proposed for several enzyme kinetics. The most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network. Since the kinetic parameters for the enzymes have been obtained from in vitro experiments, the formulation of a detailed kinetic model for the metabolic network allows parameter adjustment for in vivo conditions. We formulate an unstructured non-segregated model for a chemostat to study the dynamic response to a glucose pulse in a continuous culture of E. coli. Moreover, we perform parameter estimation by formulating a maximum likelihood problem, subject to the DAE systems, within a control vector parameterization approach. Nine kinetic parameters in the metabolic network model have been estimated with good agreement with published experimental data. For the bioreactor model, seven parameters have been tuned based on experimental data obtained in this work. Numerical results show a good agreement between the observed data and the predicted profiles.
Fil: Di Maggio, Jimena Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Paulo, Cecilia Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Estrada, Vanina Gisela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Perotti, Nora Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; Argentina
Fil: Diaz Ricci, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina
Fil: Díaz, María Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Materia
Dynamic Metabolic Network
Dynamic Optimization
Control Vector Parameterization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/27055
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/27055
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniquesDi Maggio, Jimena AndreaPaulo, Cecilia InésEstrada, Vanina GiselaPerotti, Nora InesDiaz Ricci, Juan CarlosDíaz, María SoledadDynamic Metabolic NetworkDynamic OptimizationControl Vector Parameterizationhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In the present work, we formulate parameter estimation problems for kinetic models of large-scale dynamic biotechnological systems. We propose dynamic models of increasing complexity for metabolic networks and continuous bioreactors. The differential algebraic equations (DAE) system for the metabolic network represent the glycolysis, the phosphotransferase system and the pentose-phosphate pathway of Escherichia coli, with modifications proposed for several enzyme kinetics. The most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network. Since the kinetic parameters for the enzymes have been obtained from in vitro experiments, the formulation of a detailed kinetic model for the metabolic network allows parameter adjustment for in vivo conditions. We formulate an unstructured non-segregated model for a chemostat to study the dynamic response to a glucose pulse in a continuous culture of E. coli. Moreover, we perform parameter estimation by formulating a maximum likelihood problem, subject to the DAE systems, within a control vector parameterization approach. Nine kinetic parameters in the metabolic network model have been estimated with good agreement with published experimental data. For the bioreactor model, seven parameters have been tuned based on experimental data obtained in this work. Numerical results show a good agreement between the observed data and the predicted profiles.Fil: Di Maggio, Jimena Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Paulo, Cecilia Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Estrada, Vanina Gisela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Perotti, Nora Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; ArgentinaFil: Diaz Ricci, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; ArgentinaFil: Díaz, María Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaElsevier Science Sa2013-12-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/27055Di Maggio, Jimena Andrea; Paulo, Cecilia Inés; Estrada, Vanina Gisela; Perotti, Nora Ines; Diaz Ricci, Juan Carlos; et al.; Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques; Elsevier Science Sa; Biochemical Engineering Journal; 83; 28-12-2013; 104-1151369-703XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1369703X13003598info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bej.2013.12.012info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:39:45Zoai:ri.conicet.gov.ar:11336/27055instacron: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:34982025-09-29 10:39:45.907CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
title Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
spellingShingle Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
Di Maggio, Jimena Andrea
Dynamic Metabolic Network
Dynamic Optimization
Control Vector Parameterization
title_short Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
title_full Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
title_fullStr Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
title_full_unstemmed Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
title_sort Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques
dc.creator.none.fl_str_mv Di Maggio, Jimena Andrea
Paulo, Cecilia Inés
Estrada, Vanina Gisela
Perotti, Nora Ines
Diaz Ricci, Juan Carlos
Díaz, María Soledad
author Di Maggio, Jimena Andrea
author_facet Di Maggio, Jimena Andrea
Paulo, Cecilia Inés
Estrada, Vanina Gisela
Perotti, Nora Ines
Diaz Ricci, Juan Carlos
Díaz, María Soledad
author_role author
author2 Paulo, Cecilia Inés
Estrada, Vanina Gisela
Perotti, Nora Ines
Diaz Ricci, Juan Carlos
Díaz, María Soledad
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Dynamic Metabolic Network
Dynamic Optimization
Control Vector Parameterization
topic Dynamic Metabolic Network
Dynamic Optimization
Control Vector Parameterization
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In the present work, we formulate parameter estimation problems for kinetic models of large-scale dynamic biotechnological systems. We propose dynamic models of increasing complexity for metabolic networks and continuous bioreactors. The differential algebraic equations (DAE) system for the metabolic network represent the glycolysis, the phosphotransferase system and the pentose-phosphate pathway of Escherichia coli, with modifications proposed for several enzyme kinetics. The most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network. Since the kinetic parameters for the enzymes have been obtained from in vitro experiments, the formulation of a detailed kinetic model for the metabolic network allows parameter adjustment for in vivo conditions. We formulate an unstructured non-segregated model for a chemostat to study the dynamic response to a glucose pulse in a continuous culture of E. coli. Moreover, we perform parameter estimation by formulating a maximum likelihood problem, subject to the DAE systems, within a control vector parameterization approach. Nine kinetic parameters in the metabolic network model have been estimated with good agreement with published experimental data. For the bioreactor model, seven parameters have been tuned based on experimental data obtained in this work. Numerical results show a good agreement between the observed data and the predicted profiles.
Fil: Di Maggio, Jimena Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Paulo, Cecilia Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Estrada, Vanina Gisela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Perotti, Nora Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; Argentina
Fil: Diaz Ricci, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina
Fil: Díaz, María Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
description In the present work, we formulate parameter estimation problems for kinetic models of large-scale dynamic biotechnological systems. We propose dynamic models of increasing complexity for metabolic networks and continuous bioreactors. The differential algebraic equations (DAE) system for the metabolic network represent the glycolysis, the phosphotransferase system and the pentose-phosphate pathway of Escherichia coli, with modifications proposed for several enzyme kinetics. The most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network. Since the kinetic parameters for the enzymes have been obtained from in vitro experiments, the formulation of a detailed kinetic model for the metabolic network allows parameter adjustment for in vivo conditions. We formulate an unstructured non-segregated model for a chemostat to study the dynamic response to a glucose pulse in a continuous culture of E. coli. Moreover, we perform parameter estimation by formulating a maximum likelihood problem, subject to the DAE systems, within a control vector parameterization approach. Nine kinetic parameters in the metabolic network model have been estimated with good agreement with published experimental data. For the bioreactor model, seven parameters have been tuned based on experimental data obtained in this work. Numerical results show a good agreement between the observed data and the predicted profiles.
publishDate 2013
dc.date.none.fl_str_mv 2013-12-28
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/11336/27055
Di Maggio, Jimena Andrea; Paulo, Cecilia Inés; Estrada, Vanina Gisela; Perotti, Nora Ines; Diaz Ricci, Juan Carlos; et al.; Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques; Elsevier Science Sa; Biochemical Engineering Journal; 83; 28-12-2013; 104-115
1369-703X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/27055
identifier_str_mv Di Maggio, Jimena Andrea; Paulo, Cecilia Inés; Estrada, Vanina Gisela; Perotti, Nora Ines; Diaz Ricci, Juan Carlos; et al.; Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques; Elsevier Science Sa; Biochemical Engineering Journal; 83; 28-12-2013; 104-115
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/http://www.sciencedirect.com/science/article/pii/S1369703X13003598
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bej.2013.12.012
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
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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