A General Hybrid Modeling Framework for Systems Biology Applications

Detalhes bibliográficos
Autor(a) principal: Pinto, José
Data de Publicação: 2023
Outros Autores: Ramos, João R. C., Costa, Rafael S., Oliveira, Rui
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/151582
Resumo: J.P. acknowledges a PhD grant (SFRD/BD14610472019), Fundação para a Ciência e Tecnologia (FCT).
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spelling A General Hybrid Modeling Framework for Systems Biology ApplicationsCombining Mechanistic Knowledge with Deep Neural Networks under the SBML Standardhybrid modelingdeep neural networksdeep learning; SBMLsystems biologycomputational modelingArtificial IntelligenceJ.P. acknowledges a PhD grant (SFRD/BD14610472019), Fundação para a Ciência e Tecnologia (FCT).In this paper, a computational framework is proposed that merges mechanistic modeling with deep neural networks obeying the Systems Biology Markup Language (SBML) standard. Over the last 20 years, the systems biology community has developed a large number of mechanistic models that are currently stored in public databases in SBML. With the proposed framework, existing SBML models may be redesigned into hybrid systems through the incorporation of deep neural networks into the model core, using a freely available python tool. The so-formed hybrid mechanistic/neural network models are trained with a deep learning algorithm based on the adaptive moment estimation method (ADAM), stochastic regularization and semidirect sensitivity equations. The trained hybrid models are encoded in SBML and uploaded in model databases, where they may be further analyzed as regular SBML models. This approach is illustrated with three well-known case studies: the Escherichia coli threonine synthesis model, the P58IPK signal transduction model, and the Yeast glycolytic oscillations model. The proposed framework is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.LAQV@REQUIMTEDQ - Departamento de QuímicaRUNPinto, JoséRamos, João R. C.Costa, Rafael S.Oliveira, Rui2023-04-04T22:16:30Z2023-03-012023-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/151582eng2673-2688PURE: 54633009https://doi.org/10.3390/ai4010014info: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:RCAAP2024-03-11T05:34:01Zoai:run.unl.pt:10362/151582Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:38.748479Repositório 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 A General Hybrid Modeling Framework for Systems Biology Applications
Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard
title A General Hybrid Modeling Framework for Systems Biology Applications
spellingShingle A General Hybrid Modeling Framework for Systems Biology Applications
Pinto, José
hybrid modeling
deep neural networks
deep learning; SBML
systems biology
computational modeling
Artificial Intelligence
title_short A General Hybrid Modeling Framework for Systems Biology Applications
title_full A General Hybrid Modeling Framework for Systems Biology Applications
title_fullStr A General Hybrid Modeling Framework for Systems Biology Applications
title_full_unstemmed A General Hybrid Modeling Framework for Systems Biology Applications
title_sort A General Hybrid Modeling Framework for Systems Biology Applications
author Pinto, José
author_facet Pinto, José
Ramos, João R. C.
Costa, Rafael S.
Oliveira, Rui
author_role author
author2 Ramos, João R. C.
Costa, Rafael S.
Oliveira, Rui
author2_role author
author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
DQ - Departamento de Química
RUN
dc.contributor.author.fl_str_mv Pinto, José
Ramos, João R. C.
Costa, Rafael S.
Oliveira, Rui
dc.subject.por.fl_str_mv hybrid modeling
deep neural networks
deep learning; SBML
systems biology
computational modeling
Artificial Intelligence
topic hybrid modeling
deep neural networks
deep learning; SBML
systems biology
computational modeling
Artificial Intelligence
description J.P. acknowledges a PhD grant (SFRD/BD14610472019), Fundação para a Ciência e Tecnologia (FCT).
publishDate 2023
dc.date.none.fl_str_mv 2023-04-04T22:16:30Z
2023-03-01
2023-03-01T00:00:00Z
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/10362/151582
url http://hdl.handle.net/10362/151582
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2673-2688
PURE: 54633009
https://doi.org/10.3390/ai4010014
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
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)
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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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