Predicting metabolic fluxes from omics data via machine learning

Bibliographic Details
Main Author: Gonçalves, Daniel M.
Publication Date: 2023
Other Authors: Henriques, Rui, Costa, Rafael S.
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/162653
Summary: The authors also wish to acknowledge the European Union's Horizon BioLaMer project under grant agreement number [ 101099487 ]. Publisher Copyright: © 2023 The Authors
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oai_identifier_str oai:run.unl.pt:10362/162653
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Predicting metabolic fluxes from omics data via machine learningMoving from knowledge-driven towards data-driven approachesFlux balance analysisGenome-scale modelsMetabolic fluxesOmics dataSupervised machine learningSystems biologyBiotechnologyBiophysicsStructural BiologyBiochemistryGeneticsComputer Science ApplicationsThe authors also wish to acknowledge the European Union's Horizon BioLaMer project under grant agreement number [ 101099487 ]. Publisher Copyright: © 2023 The AuthorsThe 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].LAQV@REQUIMTEDQ - Departamento de QuímicaRUNGonçalves, Daniel M.Henriques, RuiCosta, Rafael S.2024-01-22T22:55:37Z2023-10-172023-10-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/162653eng2001-0370PURE: 81977599https://doi.org/10.1016/j.csbj.2023.10.002info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:17:39Zoai:run.unl.pt:10362/162653Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:48:09.195126Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Predicting metabolic fluxes from omics data via machine learning
Moving from knowledge-driven towards data-driven approaches
title Predicting metabolic fluxes from omics data via machine learning
spellingShingle Predicting metabolic fluxes from omics data via machine learning
Gonçalves, Daniel M.
Flux balance analysis
Genome-scale models
Metabolic fluxes
Omics data
Supervised machine learning
Systems biology
Biotechnology
Biophysics
Structural Biology
Biochemistry
Genetics
Computer Science Applications
title_short Predicting metabolic fluxes from omics data via machine learning
title_full Predicting metabolic fluxes from omics data via machine learning
title_fullStr Predicting metabolic fluxes from omics data via machine learning
title_full_unstemmed Predicting metabolic fluxes from omics data via machine learning
title_sort Predicting metabolic fluxes from omics data via machine learning
author Gonçalves, Daniel M.
author_facet Gonçalves, Daniel M.
Henriques, Rui
Costa, Rafael S.
author_role author
author2 Henriques, Rui
Costa, Rafael S.
author2_role author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
DQ - Departamento de Química
RUN
dc.contributor.author.fl_str_mv Gonçalves, Daniel M.
Henriques, Rui
Costa, Rafael S.
dc.subject.por.fl_str_mv Flux balance analysis
Genome-scale models
Metabolic fluxes
Omics data
Supervised machine learning
Systems biology
Biotechnology
Biophysics
Structural Biology
Biochemistry
Genetics
Computer Science Applications
topic Flux balance analysis
Genome-scale models
Metabolic fluxes
Omics data
Supervised machine learning
Systems biology
Biotechnology
Biophysics
Structural Biology
Biochemistry
Genetics
Computer Science Applications
description The authors also wish to acknowledge the European Union's Horizon BioLaMer project under grant agreement number [ 101099487 ]. Publisher Copyright: © 2023 The Authors
publishDate 2023
dc.date.none.fl_str_mv 2023-10-17
2023-10-17T00:00:00Z
2024-01-22T22:55:37Z
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/10362/162653
url http://hdl.handle.net/10362/162653
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2001-0370
PURE: 81977599
https://doi.org/10.1016/j.csbj.2023.10.002
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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