Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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/1822/56416 |
Resumo: | Genome-Scale Metabolic Models (GSMMs), mathematical representations of the cell metabolism in different organisms including humans, are resourceful tools to simulate metabolic phenotypes and understand associated diseases, such as obesity, diabetes and cancer. In the last years, different algorithms have been developed to generate tissue-specific metabolic models that simulate different phenotypes for distinct cell types. Hepatocyte cells are one of the main sites of metabolic conversions, mainly due to their diverse physiological functions. Most of the liver's tissue is formed by hepatocytes, being one of the largest and most important organs regarding its biological functions. Hepatocellular carcinoma is, also, one of the most important human cancers with high mortality rates. In this study, we will analyze four different algorithms (MBA, mCADRE, tINIT and FASTCORE) for tissue-specific model reconstruction, based on a template model and two types of data sources: transcriptomics and proteomics. These methods will be applied to the reconstruction of metabolic models for hepatocyte cells and HepG2 cancer cell line. The models will be analyzed and compared under different perspectives, emphasizing their functional analysis considering a set of metabolic liver tasks. The results show that there is no ``ideal'' algorithm. However, with the current analysis, we were able to retrieve knowledge about the metabolism of the liver. |
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Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cellsTissue-specific genome-scale metabolic modelsLiver metabolismHepatocellular carcinomaScience & TechnologyGenome-Scale Metabolic Models (GSMMs), mathematical representations of the cell metabolism in different organisms including humans, are resourceful tools to simulate metabolic phenotypes and understand associated diseases, such as obesity, diabetes and cancer. In the last years, different algorithms have been developed to generate tissue-specific metabolic models that simulate different phenotypes for distinct cell types. Hepatocyte cells are one of the main sites of metabolic conversions, mainly due to their diverse physiological functions. Most of the liver's tissue is formed by hepatocytes, being one of the largest and most important organs regarding its biological functions. Hepatocellular carcinoma is, also, one of the most important human cancers with high mortality rates. In this study, we will analyze four different algorithms (MBA, mCADRE, tINIT and FASTCORE) for tissue-specific model reconstruction, based on a template model and two types of data sources: transcriptomics and proteomics. These methods will be applied to the reconstruction of metabolic models for hepatocyte cells and HepG2 cancer cell line. The models will be analyzed and compared under different perspectives, emphasizing their functional analysis considering a set of metabolic liver tasks. The results show that there is no ``ideal'' algorithm. However, with the current analysis, we were able to retrieve knowledge about the metabolism of the liver.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684), BioTecNorte operation (NORTE01-0145-FEDER-000004) and Search-ON2: Revitalization of HPC infrastructure of UMinho, (NORTE-07-0162-FEDER-000086), all funded by European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersionSpringer NatureUniversidade do MinhoFerreira, JorgeCorreia, SaraRocha, Miguel2017-032017-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/56416engFerreira, Jorge; Correia, Sara; Rocha, Miguel, Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells. Interdisciplinary Sciences-Computational Life Sciences, 9(1), 36-45, 20171913-27511867-146210.1007/s12539-017-0214-y28255832https://link.springer.com/journal/12539info: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:RCAAP2023-07-21T12:22:31Zoai:repositorium.sdum.uminho.pt:1822/56416Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:16:01.640449Repositório 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 |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells |
title |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells |
spellingShingle |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells Ferreira, Jorge Tissue-specific genome-scale metabolic models Liver metabolism Hepatocellular carcinoma Science & Technology |
title_short |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells |
title_full |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells |
title_fullStr |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells |
title_full_unstemmed |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells |
title_sort |
Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells |
author |
Ferreira, Jorge |
author_facet |
Ferreira, Jorge Correia, Sara Rocha, Miguel |
author_role |
author |
author2 |
Correia, Sara Rocha, Miguel |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Ferreira, Jorge Correia, Sara Rocha, Miguel |
dc.subject.por.fl_str_mv |
Tissue-specific genome-scale metabolic models Liver metabolism Hepatocellular carcinoma Science & Technology |
topic |
Tissue-specific genome-scale metabolic models Liver metabolism Hepatocellular carcinoma Science & Technology |
description |
Genome-Scale Metabolic Models (GSMMs), mathematical representations of the cell metabolism in different organisms including humans, are resourceful tools to simulate metabolic phenotypes and understand associated diseases, such as obesity, diabetes and cancer. In the last years, different algorithms have been developed to generate tissue-specific metabolic models that simulate different phenotypes for distinct cell types. Hepatocyte cells are one of the main sites of metabolic conversions, mainly due to their diverse physiological functions. Most of the liver's tissue is formed by hepatocytes, being one of the largest and most important organs regarding its biological functions. Hepatocellular carcinoma is, also, one of the most important human cancers with high mortality rates. In this study, we will analyze four different algorithms (MBA, mCADRE, tINIT and FASTCORE) for tissue-specific model reconstruction, based on a template model and two types of data sources: transcriptomics and proteomics. These methods will be applied to the reconstruction of metabolic models for hepatocyte cells and HepG2 cancer cell line. The models will be analyzed and compared under different perspectives, emphasizing their functional analysis considering a set of metabolic liver tasks. The results show that there is no ``ideal'' algorithm. However, with the current analysis, we were able to retrieve knowledge about the metabolism of the liver. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-03 2017-03-01T00:00:00Z |
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/1822/56416 |
url |
http://hdl.handle.net/1822/56416 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ferreira, Jorge; Correia, Sara; Rocha, Miguel, Analysing algorithms and data sources for the tissue-specific reconstruction of liver healthy and cancer cells. Interdisciplinary Sciences-Computational Life Sciences, 9(1), 36-45, 2017 1913-2751 1867-1462 10.1007/s12539-017-0214-y 28255832 https://link.springer.com/journal/12539 |
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 |
Springer Nature |
publisher.none.fl_str_mv |
Springer Nature |
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 |
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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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1799132607659638784 |