Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/42762 |
Resumo: | Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models. |
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Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype predictionScience & TechnologyGenome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Public Library of ScienceUniversidade do MinhoMachado, DanielHerrgård, Markus J.Rocha, Isabel2016-102016-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/42762engMachado, Daniel; Herrgård, Markus J.; Rocha, Isabel, Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction. PLoS Computational Biology, 12(10:e1005140), 1-24, 20161553-734X1553-735810.1371/journal.pcbi.100514027711110info: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:RCAAP2023-07-21T12:08:52Zoai:repositorium.sdum.uminho.pt:1822/42762Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:00:09.322414Repositório 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 |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction |
title |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction |
spellingShingle |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction Machado, Daniel Science & Technology |
title_short |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction |
title_full |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction |
title_fullStr |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction |
title_full_unstemmed |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction |
title_sort |
Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction |
author |
Machado, Daniel |
author_facet |
Machado, Daniel Herrgård, Markus J. Rocha, Isabel |
author_role |
author |
author2 |
Herrgård, Markus J. Rocha, Isabel |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Machado, Daniel Herrgård, Markus J. Rocha, Isabel |
dc.subject.por.fl_str_mv |
Science & Technology |
topic |
Science & Technology |
description |
Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-10 2016-10-01T00:00:00Z |
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 |
http://hdl.handle.net/1822/42762 |
url |
http://hdl.handle.net/1822/42762 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Machado, Daniel; Herrgård, Markus J.; Rocha, Isabel, Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction. PLoS Computational Biology, 12(10:e1005140), 1-24, 2016 1553-734X 1553-7358 10.1371/journal.pcbi.1005140 27711110 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Public Library of Science |
publisher.none.fl_str_mv |
Public Library of Science |
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 |
repository.mail.fl_str_mv |
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1799132395738234880 |