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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/27055
Ver los metadatos del registro completo
id |
CONICETDig_95aa12b04d88bcedeb925b59e117c72f |
---|---|
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 Técnicas2025-09-03T10:09:24Zoai: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:34982025-09-03 10:09:24.729CONICET 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 |
_version_ |
1842270079356502016 |
score |
13.13397 |