Sequential design of dynamic experiments in modeling for optimization of biological processes

Autores
Cristaldi, Mariano Daniel; Grau, Ricardo José Antonio; Martínez, Ernesto Carlos
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. A methodology for model-based design of dynamic experiments in modeling for optimization is proposed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 whose DNA has been cloned in an E. coli strain. A library of tendency models is used to increasingly bias bioreactor operating conditions towards an optimum. Parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA). At each iteration, the ´most informative´ tendency model is used for designining the next dynamic experiment. Model selection is based on minimizing an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.
Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Grau, Ricardo José Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
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
Materia
Dynamic Experiments
Experimental Design
Modeling
Optimization
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:11336/83834
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Sequential design of dynamic experiments in modeling for optimization of biological processesCristaldi, Mariano DanielGrau, Ricardo José AntonioMartínez, Ernesto CarlosDynamic ExperimentsExperimental DesignModelingOptimizationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. A methodology for model-based design of dynamic experiments in modeling for optimization is proposed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 whose DNA has been cloned in an E. coli strain. A library of tendency models is used to increasingly bias bioreactor operating conditions towards an optimum. Parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA). At each iteration, the ´most informative´ tendency model is used for designining the next dynamic experiment. Model selection is based on minimizing an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Grau, Ricardo José Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: 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; ArgentinaElsevier B.V.2009-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/83834Cristaldi, Mariano Daniel; Grau, Ricardo José Antonio; Martínez, Ernesto Carlos; Sequential design of dynamic experiments in modeling for optimization of biological processes; Elsevier B.V.; Computer Aided Chemical Engineering; 27; C; 8-2009; 369-3741570-7946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/S1570-7946(09)70282-2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:42:30Zoai:ri.conicet.gov.ar:11336/83834instacron: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 10:42:31.228CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sequential design of dynamic experiments in modeling for optimization of biological processes
title Sequential design of dynamic experiments in modeling for optimization of biological processes
spellingShingle Sequential design of dynamic experiments in modeling for optimization of biological processes
Cristaldi, Mariano Daniel
Dynamic Experiments
Experimental Design
Modeling
Optimization
title_short Sequential design of dynamic experiments in modeling for optimization of biological processes
title_full Sequential design of dynamic experiments in modeling for optimization of biological processes
title_fullStr Sequential design of dynamic experiments in modeling for optimization of biological processes
title_full_unstemmed Sequential design of dynamic experiments in modeling for optimization of biological processes
title_sort Sequential design of dynamic experiments in modeling for optimization of biological processes
dc.creator.none.fl_str_mv Cristaldi, Mariano Daniel
Grau, Ricardo José Antonio
Martínez, Ernesto Carlos
author Cristaldi, Mariano Daniel
author_facet Cristaldi, Mariano Daniel
Grau, Ricardo José Antonio
Martínez, Ernesto Carlos
author_role author
author2 Grau, Ricardo José Antonio
Martínez, Ernesto Carlos
author2_role author
author
dc.subject.none.fl_str_mv Dynamic Experiments
Experimental Design
Modeling
Optimization
topic Dynamic Experiments
Experimental Design
Modeling
Optimization
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. A methodology for model-based design of dynamic experiments in modeling for optimization is proposed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 whose DNA has been cloned in an E. coli strain. A library of tendency models is used to increasingly bias bioreactor operating conditions towards an optimum. Parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA). At each iteration, the ´most informative´ tendency model is used for designining the next dynamic experiment. Model selection is based on minimizing an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.
Fil: Cristaldi, Mariano Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Grau, Ricardo José Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
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
description Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. A methodology for model-based design of dynamic experiments in modeling for optimization is proposed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 whose DNA has been cloned in an E. coli strain. A library of tendency models is used to increasingly bias bioreactor operating conditions towards an optimum. Parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA). At each iteration, the ´most informative´ tendency model is used for designining the next dynamic experiment. Model selection is based on minimizing an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.
publishDate 2009
dc.date.none.fl_str_mv 2009-08
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/11336/83834
Cristaldi, Mariano Daniel; Grau, Ricardo José Antonio; Martínez, Ernesto Carlos; Sequential design of dynamic experiments in modeling for optimization of biological processes; Elsevier B.V.; Computer Aided Chemical Engineering; 27; C; 8-2009; 369-374
1570-7946
CONICET Digital
CONICET
url http://hdl.handle.net/11336/83834
identifier_str_mv Cristaldi, Mariano Daniel; Grau, Ricardo José Antonio; Martínez, Ernesto Carlos; Sequential design of dynamic experiments in modeling for optimization of biological processes; Elsevier B.V.; Computer Aided Chemical Engineering; 27; C; 8-2009; 369-374
1570-7946
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/S1570-7946(09)70282-2
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
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
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_ 1844614458107756544
score 13.070432

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