Global dynamic optimization approach to predict activation in metabolic pathways

[Background] During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this op...

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Detalhes bibliográficos
Autores: Hijas-Liste, G. M., Klipp, Edda, Balsa-Canto, Eva, Banga, Julio R.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2014
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/95023
Acesso em linha:http://hdl.handle.net/10261/95023
Access Level:acceso abierto
Palavra-chave:Dynamic optimization
Global optimization
Multi-objective optimization
Pareto optimality
Metabolic pathways
Gene expression
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spelling Global dynamic optimization approach to predict activation in metabolic pathwaysHijas-Liste, G. M.Klipp, EddaBalsa-Canto, EvaBanga, Julio R.Dynamic optimizationGlobal optimizationMulti-objective optimizationPareto optimalityMetabolic pathwaysGene expression[Background] During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework.[Results] In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results.[Conclusions] The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.This research received financial support from the Spanish Ministerio de Economía y Competitividad (and the FEDER) through the project “MultiScales” (DPI2011-28112-C04-03), and from the CSIC intramural project “BioREDES” (PIE-201170E018). Gundián M. de Hijas Liste acknowledges financial support from the MICINN-FPI programmePeer ReviewedBioMed CentralMinisterio de Economía y Competitividad (España)European CommissionConsejo Superior de Investigaciones Científicas (España)Ministerio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2014201420142014info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/95023reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1186/1752-0509-8-1Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/950232026-05-22T06:33:51Z
dc.title.none.fl_str_mv Global dynamic optimization approach to predict activation in metabolic pathways
title Global dynamic optimization approach to predict activation in metabolic pathways
spellingShingle Global dynamic optimization approach to predict activation in metabolic pathways
Hijas-Liste, G. M.
Dynamic optimization
Global optimization
Multi-objective optimization
Pareto optimality
Metabolic pathways
Gene expression
title_short Global dynamic optimization approach to predict activation in metabolic pathways
title_full Global dynamic optimization approach to predict activation in metabolic pathways
title_fullStr Global dynamic optimization approach to predict activation in metabolic pathways
title_full_unstemmed Global dynamic optimization approach to predict activation in metabolic pathways
title_sort Global dynamic optimization approach to predict activation in metabolic pathways
dc.creator.none.fl_str_mv Hijas-Liste, G. M.
Klipp, Edda
Balsa-Canto, Eva
Banga, Julio R.
author Hijas-Liste, G. M.
author_facet Hijas-Liste, G. M.
Klipp, Edda
Balsa-Canto, Eva
Banga, Julio R.
author_role author
author2 Klipp, Edda
Balsa-Canto, Eva
Banga, Julio R.
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
European Commission
Consejo Superior de Investigaciones Científicas (España)
Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Dynamic optimization
Global optimization
Multi-objective optimization
Pareto optimality
Metabolic pathways
Gene expression
topic Dynamic optimization
Global optimization
Multi-objective optimization
Pareto optimality
Metabolic pathways
Gene expression
description [Background] During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014
2014
2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/95023
url http://hdl.handle.net/10261/95023
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1186/1752-0509-8-1
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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