Identification of neutral biochemical network models from time series data

Detalhes bibliográficos
Autor(a) principal: Vilela, Marco
Data de Publicação: 2009
Outros Autores: Vinga, Susana, Maia, Marco A.Grivet Mattoso, Voit, Eberhard O., Almeida, Jonas S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.1186/1752-0509-3-47
Resumo: info:eu-repo/grantAgreement/FCT/3599-PPCDT/69530/PT The authors acknowledge partial support by project DynaMo (PTDC/EEA-ACR/69530/2006; S. Vinga, PI) from the Portuguese Science Foundation (FCT) and the INESC-ID. We also would like to thank the anonymous reviewers for the constructive suggestions. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring institutions.
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spelling Identification of neutral biochemical network models from time series dataS-SYSTEM MODELSPOWER-LAW APPROXIMATIONPARAMETER-ESTIMATIONBIOLOGICAL NETWORKSSTATISTICAL-METHODSIDENTIFIABILITYOPTIMIZATIONDYNAMICSROBUSTNESSALGORITHMStructural BiologyModelling and SimulationMolecular BiologyComputer Science ApplicationsApplied Mathematicsinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/69530/PT The authors acknowledge partial support by project DynaMo (PTDC/EEA-ACR/69530/2006; S. Vinga, PI) from the Portuguese Science Foundation (FCT) and the INESC-ID. We also would like to thank the anonymous reviewers for the constructive suggestions. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring institutions.Background: The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results: In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion: The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNVilela, MarcoVinga, SusanaMaia, Marco A.Grivet MattosoVoit, Eberhard O.Almeida, Jonas S.2017-09-14T22:03:07Z2009-05-052009-05-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttps://doi.org/10.1186/1752-0509-3-47eng1752-0509PURE: 3129619http://www.scopus.com/inward/record.url?scp=67649663892&partnerID=8YFLogxKhttps://doi.org/10.1186/1752-0509-3-47info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:11:30Zoai:run.unl.pt:10362/23261Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:27:43.745523Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Identification of neutral biochemical network models from time series data
title Identification of neutral biochemical network models from time series data
spellingShingle Identification of neutral biochemical network models from time series data
Vilela, Marco
S-SYSTEM MODELS
POWER-LAW APPROXIMATION
PARAMETER-ESTIMATION
BIOLOGICAL NETWORKS
STATISTICAL-METHODS
IDENTIFIABILITY
OPTIMIZATION
DYNAMICS
ROBUSTNESS
ALGORITHM
Structural Biology
Modelling and Simulation
Molecular Biology
Computer Science Applications
Applied Mathematics
title_short Identification of neutral biochemical network models from time series data
title_full Identification of neutral biochemical network models from time series data
title_fullStr Identification of neutral biochemical network models from time series data
title_full_unstemmed Identification of neutral biochemical network models from time series data
title_sort Identification of neutral biochemical network models from time series data
author Vilela, Marco
author_facet Vilela, Marco
Vinga, Susana
Maia, Marco A.Grivet Mattoso
Voit, Eberhard O.
Almeida, Jonas S.
author_role author
author2 Vinga, Susana
Maia, Marco A.Grivet Mattoso
Voit, Eberhard O.
Almeida, Jonas S.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
RUN
dc.contributor.author.fl_str_mv Vilela, Marco
Vinga, Susana
Maia, Marco A.Grivet Mattoso
Voit, Eberhard O.
Almeida, Jonas S.
dc.subject.por.fl_str_mv S-SYSTEM MODELS
POWER-LAW APPROXIMATION
PARAMETER-ESTIMATION
BIOLOGICAL NETWORKS
STATISTICAL-METHODS
IDENTIFIABILITY
OPTIMIZATION
DYNAMICS
ROBUSTNESS
ALGORITHM
Structural Biology
Modelling and Simulation
Molecular Biology
Computer Science Applications
Applied Mathematics
topic S-SYSTEM MODELS
POWER-LAW APPROXIMATION
PARAMETER-ESTIMATION
BIOLOGICAL NETWORKS
STATISTICAL-METHODS
IDENTIFIABILITY
OPTIMIZATION
DYNAMICS
ROBUSTNESS
ALGORITHM
Structural Biology
Modelling and Simulation
Molecular Biology
Computer Science Applications
Applied Mathematics
description info:eu-repo/grantAgreement/FCT/3599-PPCDT/69530/PT The authors acknowledge partial support by project DynaMo (PTDC/EEA-ACR/69530/2006; S. Vinga, PI) from the Portuguese Science Foundation (FCT) and the INESC-ID. We also would like to thank the anonymous reviewers for the constructive suggestions. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring institutions.
publishDate 2009
dc.date.none.fl_str_mv 2009-05-05
2009-05-05T00:00:00Z
2017-09-14T22:03:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.1186/1752-0509-3-47
url https://doi.org/10.1186/1752-0509-3-47
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1752-0509
PURE: 3129619
http://www.scopus.com/inward/record.url?scp=67649663892&partnerID=8YFLogxK
https://doi.org/10.1186/1752-0509-3-47
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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