Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization

[EN] Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological coin portents. The increasing complexity of the circuits being designed requires performing approximatio...

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Autores: Boada-Acosta, Yadira Fernanda|||0000-0001-5677-2702, Vignoni, Alejandro|||0000-0001-9977-7132, Picó, Jesús|||0000-0003-4144-3521, Reynoso Meza, Gilberto
Tipo de recurso: artículo
Fecha de publicación:2016
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/150443
Acceso en línea:https://riunet.upv.es/handle/10251/150443
Access Level:acceso abierto
Palabra clave:Biological circuits
Kinetic parameters
Parameter identification
Multi-objective
Optimization
INGENIERIA DE SISTEMAS Y AUTOMATICA
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dc.title.none.fl_str_mv Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
title Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
spellingShingle Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
Boada-Acosta, Yadira Fernanda|||0000-0001-5677-2702
Biological circuits
Kinetic parameters
Parameter identification
Multi-objective
Optimization
INGENIERIA DE SISTEMAS Y AUTOMATICA
title_short Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
title_full Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
title_fullStr Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
title_full_unstemmed Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
title_sort Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization
dc.creator.none.fl_str_mv Boada-Acosta, Yadira Fernanda|||0000-0001-5677-2702
Vignoni, Alejandro|||0000-0001-9977-7132
Picó, Jesús|||0000-0003-4144-3521
Reynoso Meza, Gilberto
author Boada-Acosta, Yadira Fernanda|||0000-0001-5677-2702
author_facet Boada-Acosta, Yadira Fernanda|||0000-0001-5677-2702
Vignoni, Alejandro|||0000-0001-9977-7132
Picó, Jesús|||0000-0003-4144-3521
Reynoso Meza, Gilberto
author_role author
author2 Vignoni, Alejandro|||0000-0001-9977-7132
Picó, Jesús|||0000-0003-4144-3521
Reynoso Meza, Gilberto
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería de Sistemas y Automática
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Industrial
Santander Universidades
European Regional Development Fund
Universitat Politècnica de València
Ministerio de Economía y Competitividad
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil
Ministerio de Ciencia e Innovación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Biological circuits
Kinetic parameters
Parameter identification
Multi-objective
Optimization
INGENIERIA DE SISTEMAS Y AUTOMATICA
topic Biological circuits
Kinetic parameters
Parameter identification
Multi-objective
Optimization
INGENIERIA DE SISTEMAS Y AUTOMATICA
description [EN] Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological coin portents. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. (C) 2016 IFAC (International Federation Of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/150443
url https://riunet.upv.es/handle/10251/150443
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 BJT%2F304804%2F2014-2
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 DPI2014-55276-C5-1-R BIOLOGIA SINTETICA PARA LA MEJORA EN BIOPRODUCCION: DISEÑO, OPTIMIZACION, MONITORIZACION Y CONTROL
Universitat Politècnica de València https://doi.org/10.13039/501100004233 FPI%2F2013-3242
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 UPOV13-3E-1889 Unidad de implementación y caracterización de circuitos biológicos sintéticos
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 DPI2011-28112-C04-01 MONITORIZACION, INFERENCIA, OPTIMIZACION Y CONTROL MULTI-ESCALA: DE CELULAS A BIORREACTORES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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spelling Parameter Identification in Synthetic Biological Circuits Using Multi-Objective OptimizationBoada-Acosta, Yadira Fernanda|||0000-0001-5677-2702Vignoni, Alejandro|||0000-0001-9977-7132Picó, Jesús|||0000-0003-4144-3521Reynoso Meza, GilbertoBiological circuitsKinetic parametersParameter identificationMulti-objectiveOptimizationINGENIERIA DE SISTEMAS Y AUTOMATICA[EN] Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological coin portents. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. (C) 2016 IFAC (International Federation Of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1). Y.B. thanks grant FP/2013-3242 of Universitat Politecnica de Valencia and Becas Iberoamerica of Santander Group, Spain 2015. G.R.M. thanks the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. We are grateful to Dr. C,Bauerl and Dr, D. Provencio at the SB2CLab for their help in plasmid construction and getting experimental data. Also to Dr. V. Monedero at IATACSIC for allowing us to use the POLARstar plate reader at his lab,ElsevierDepartamento de Ingeniería de Sistemas y AutomáticaEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería IndustrialSantander UniversidadesEuropean Regional Development FundUniversitat Politècnica de ValènciaMinisterio de Economía y CompetitividadConselho Nacional de Desenvolvimento Científico e Tecnológico, BrasilMinisterio de Ciencia e InnovaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20162016-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/150443reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengConselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 BJT%2F304804%2F2014-2Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 DPI2014-55276-C5-1-R BIOLOGIA SINTETICA PARA LA MEJORA EN BIOPRODUCCION: DISEÑO, OPTIMIZACION, MONITORIZACION Y CONTROLUniversitat Politècnica de València https://doi.org/10.13039/501100004233 FPI%2F2013-3242Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 UPOV13-3E-1889 Unidad de implementación y caracterización de circuitos biológicos sintéticosMinisterio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 DPI2011-28112-C04-01 MONITORIZACION, INFERENCIA, OPTIMIZACION Y CONTROL MULTI-ESCALA: DE CELULAS A BIORREACTORESopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1504432026-06-13T07:49:27Z
score 15,30478

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