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Predicting metabolic fluxes from omics data via machine learning

2023-10-17Artigo científico Acesso aberto
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dc.contributor.authorGonçalves, Daniel M.
dc.contributor.authorHenriques, Rui
dc.contributor.authorCosta, Rafael S.
dc.contributor.institutionLAQV@REQUIMTE
dc.contributor.institutionDQ - Departamento de Química
dc.contributor.pblElsevier BV
dc.date.accessioned2024-01-22T22:55:37Z
dc.date.available2024-01-22T22:55:37Z
dc.date.issued2023-10-17
dc.descriptionThe authors also wish to acknowledge the European Union's Horizon BioLaMer project under grant agreement number [ 101099487 ]. Publisher Copyright: © 2023 The Authors
dc.description.abstractThe accurate prediction of phenotypes in microorganisms is a main challenge for systems biology. Genome-scale models (GEMs) are a widely used mathematical formalism for predicting metabolic fluxes using constraint-based modeling methods such as flux balance analysis (FBA). However, they require prior knowledge of the metabolic network of an organism and appropriate objective functions, often hampering the prediction of metabolic fluxes under different conditions. Moreover, the integration of omics data to improve the accuracy of phenotype predictions in different physiological states is still in its infancy. Here, we present a novel approach for predicting fluxes under various conditions. We explore the use of supervised machine learning (ML) models using transcriptomics and/or proteomics data and compare their performance against the standard parsimonious FBA (pFBA) approach using case studies of Escherichia coli organism as an example. Our results show that the proposed omics-based ML approach is promising to predict both internal and external metabolic fluxes with smaller prediction errors in comparison to the pFBA approach. The code, data, and detailed results are available at the project's repository [1].en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent14
dc.format.extent1745516
dc.identifier.doi10.1016/j.csbj.2023.10.002
dc.identifier.issn2001-0370
dc.identifier.otherPURE: 81977599
dc.identifier.otherPURE UUID: bb9f98a1-8e45-496e-9db2-f91ff7222f50
dc.identifier.otherScopus: 85174449267
dc.identifier.otherWOS: 001096771800001
dc.identifier.otherPubMed: 37876626
dc.identifier.otherPubMedCentral: PMC10590844
dc.identifier.otherORCID: /0000-0002-7539-488X/work/151424027
dc.identifier.urihttp://hdl.handle.net/10362/162653
dc.identifier.urlhttps://www.scopus.com/pages/publications/85174449267
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT//2022.12633.BD/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50006%2F2020/PT
dc.relationAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
dc.relationAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relationNot Available
dc.relationinfo:eu-repo/grantAgreement/FCT/CEEC IND 2017/CEECIND%2F01399%2F2017%2FCP1462%2FCT0015/PT
dc.subjectFlux balance analysis
dc.subjectGenome-scale models
dc.subjectMetabolic fluxes
dc.subjectOmics data
dc.subjectSupervised machine learning
dc.subjectSystems biology
dc.subjectBiotechnology
dc.subjectBiophysics
dc.subjectStructural Biology
dc.subjectBiochemistry
dc.subjectGenetics
dc.subjectComputer Science Applications
dc.titlePredicting metabolic fluxes from omics data via machine learningen
dc.title.subtitleMoving from knowledge-driven towards data-driven approachesen
dc.typejournal article
degois.publication.firstPage4960
degois.publication.lastPage4973
degois.publication.titleComputational and Structural Biotechnology Journal
degois.publication.volume21
dspace.entity.typePublication
oaire.awardNumberUIDB/50006/2020
oaire.awardNumberUIDP/50006/2020
oaire.awardNumberUIDB/50021/2020
oaire.awardNumberCEECIND/01399/2017/CP1462/CT0015
oaire.awardTitleAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
oaire.awardTitleAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardTitleNot Available
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50006%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50006%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND 2017/CEECIND%2F01399%2F2017%2FCP1462%2FCT0015/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamCEEC IND 2017
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicationadc84c24-ba1d-4bcd-b753-2128ce9a5faa
relation.isProjectOfPublication4d9a4d40-4803-4f3a-976b-d6eaaef42510
relation.isProjectOfPublication1cf59518-2425-4137-a914-53580cbf4712
relation.isProjectOfPublication61186329-b5eb-4bc8-adb0-e7f48ae5a1c0
relation.isProjectOfPublication.latestForDiscovery61186329-b5eb-4bc8-adb0-e7f48ae5a1c0

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