Exploring synergies between plant metabolic modelling and machine learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/77194 |
Resumo: | As plants produce an enormous diversity of metabolites to help them adapt to the environment, the study of plant metabolism is of utmost importance to understand different plant phenotypes. Omics data have been generated at an unprecedented rate for several organisms, including plants, and are widely used to study the central dogma of molecular biology, connecting the genome to phenotypes. Constraint-based modelling (CBM) methods, working over genome-scale metabolic models (GSMMs), have been crucial for organising and analysing omics data by integrating them with biochemical knowledge. In 2009, the first plant GSMM was reconstructed and, since then, several advances have been made, including the creation of context- and multi-tissue models that have supported the study of plant metabolism. Nevertheless, plant metabolic modelling remains very challenging. In parallel, as omics datasets are complex and heterogeneous, machine learning (ML) models have been applied in their interpretation to foster knowledge discovery. Recently, the first studies combining both CBM and ML approaches have emerged and have shown promising results. Here, we present the major advances in plant metabolic modelling and review the main CBM-ML hybrid studies. Finally, we discuss the application of machine learning to address the unique challenges of plant metabolic modelling. |
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Exploring synergies between plant metabolic modelling and machine learningConstraint-based modellingPlant genome-scale metabolic modelsOmics dataMachine learningScience & TechnologyAs plants produce an enormous diversity of metabolites to help them adapt to the environment, the study of plant metabolism is of utmost importance to understand different plant phenotypes. Omics data have been generated at an unprecedented rate for several organisms, including plants, and are widely used to study the central dogma of molecular biology, connecting the genome to phenotypes. Constraint-based modelling (CBM) methods, working over genome-scale metabolic models (GSMMs), have been crucial for organising and analysing omics data by integrating them with biochemical knowledge. In 2009, the first plant GSMM was reconstructed and, since then, several advances have been made, including the creation of context- and multi-tissue models that have supported the study of plant metabolism. Nevertheless, plant metabolic modelling remains very challenging. In parallel, as omics datasets are complex and heterogeneous, machine learning (ML) models have been applied in their interpretation to foster knowledge discovery. Recently, the first studies combining both CBM and ML approaches have emerged and have shown promising results. Here, we present the major advances in plant metabolic modelling and review the main CBM-ML hybrid studies. Finally, we discuss the application of machine learning to address the unique challenges of plant metabolic modelling.This work was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2020 unit and the PhD scholarship (SFRH/BD/144643/2019) to Marta Sampaio. Oscar Dias also acknowledges FCT for the Assistant Research contract obtained under CEEC Individual 2018.info:eu-repo/semantics/publishedVersionElsevierUniversidade do MinhoSampaio, MartaRocha, MiguelDias, Oscar2022-04-162022-04-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/77194engSampaio, Marta; Rocha, Miguel; Dias, Oscar, Exploring synergies between plant metabolic modelling and machine learning. Computational and Structural Biotechnology Journal, 20, 1885-1900, 20222001-037010.1016/j.csbj.2022.04.016https://www.sciencedirect.com/journal/computational-and-structural-biotechnology-journalinfo: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:RCAAP2023-07-21T12:36:06Zoai:repositorium.sdum.uminho.pt:1822/77194Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:32:05.450330Repositório 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 |
Exploring synergies between plant metabolic modelling and machine learning |
title |
Exploring synergies between plant metabolic modelling and machine learning |
spellingShingle |
Exploring synergies between plant metabolic modelling and machine learning Sampaio, Marta Constraint-based modelling Plant genome-scale metabolic models Omics data Machine learning Science & Technology |
title_short |
Exploring synergies between plant metabolic modelling and machine learning |
title_full |
Exploring synergies between plant metabolic modelling and machine learning |
title_fullStr |
Exploring synergies between plant metabolic modelling and machine learning |
title_full_unstemmed |
Exploring synergies between plant metabolic modelling and machine learning |
title_sort |
Exploring synergies between plant metabolic modelling and machine learning |
author |
Sampaio, Marta |
author_facet |
Sampaio, Marta Rocha, Miguel Dias, Oscar |
author_role |
author |
author2 |
Rocha, Miguel Dias, Oscar |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Sampaio, Marta Rocha, Miguel Dias, Oscar |
dc.subject.por.fl_str_mv |
Constraint-based modelling Plant genome-scale metabolic models Omics data Machine learning Science & Technology |
topic |
Constraint-based modelling Plant genome-scale metabolic models Omics data Machine learning Science & Technology |
description |
As plants produce an enormous diversity of metabolites to help them adapt to the environment, the study of plant metabolism is of utmost importance to understand different plant phenotypes. Omics data have been generated at an unprecedented rate for several organisms, including plants, and are widely used to study the central dogma of molecular biology, connecting the genome to phenotypes. Constraint-based modelling (CBM) methods, working over genome-scale metabolic models (GSMMs), have been crucial for organising and analysing omics data by integrating them with biochemical knowledge. In 2009, the first plant GSMM was reconstructed and, since then, several advances have been made, including the creation of context- and multi-tissue models that have supported the study of plant metabolism. Nevertheless, plant metabolic modelling remains very challenging. In parallel, as omics datasets are complex and heterogeneous, machine learning (ML) models have been applied in their interpretation to foster knowledge discovery. Recently, the first studies combining both CBM and ML approaches have emerged and have shown promising results. Here, we present the major advances in plant metabolic modelling and review the main CBM-ML hybrid studies. Finally, we discuss the application of machine learning to address the unique challenges of plant metabolic modelling. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-16 2022-04-16T00:00:00Z |
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 |
https://hdl.handle.net/1822/77194 |
url |
https://hdl.handle.net/1822/77194 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sampaio, Marta; Rocha, Miguel; Dias, Oscar, Exploring synergies between plant metabolic modelling and machine learning. Computational and Structural Biotechnology Journal, 20, 1885-1900, 2022 2001-0370 10.1016/j.csbj.2022.04.016 https://www.sciencedirect.com/journal/computational-and-structural-biotechnology-journal |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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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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1799132832504741888 |