Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do LNCC |
Texto Completo: | https://tede.lncc.br/handle/tede/307 |
Resumo: | Genome-scale metabolic reconstructions are typically created from an annotated genome. However, for not knowing all the functions of the annotated genes, or all the genes in the genome, it is not known all possible reactions that exist in a organism, making metabolic models systematically incomplete. Evidence of this fact is that, if models are created only using an annotated genome, it will be unable to "grow" in conditions where growth was observed in vitro. For the model to "grow" in silico it is necessary to gap fill the model, adding reactions to the model that were not identified during the genome annotation, allowing previously blocked reactions to be activated. This change occurs because the added reactions allowed the substrates of the blocked reactions to be produced, or because they prevented products from the blocked reactions to accumulate inside the network. Genome-scale metabolic models can be semi automatically created using tools that require very little user's experience. However, models created by such tools use metabolites and reactions identifiers that are not compatible with identifiers from KEGG and BiGG Models, what is necessary for the gap filling procedure. Additional problem is the comparison between draft and curated models, since curated models often use BiGG Models identifiers, while automatically created models usually use SEED's. The present work proposes a methodology to translate draft models, from SEED identifiers to BiGG Models identifiers, and translate the KEGG reactions database, from the KEGG identifiers to the BiGG Models identifiers. So that these reactions can be easily used to gap fill the model. Also, based on reactions used by models available in the BiGG Models' database, an interactive menu was created to allow the user to build a customized set of reactions to be used during the gap filling. Finally, all the translated reactions must be stoichiometrically balanced. With the draft model and the reaction dataset created, the gap filling can be performed. The gap filling method developed in this work uses a newly developed method for metabolic network expansion, while more traditional gap filling methods are based on FBA. Therefore, we first studied the necessary conditions for the FBA results and the metabolic networks expansion to be equivalent. With these conditions assured, the method focus on unblocking from the draft model as many original reactions as possible, finding a solution that minimizes the number of reactions added to the model’s final version. |
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Nicolás, Marisa FabianaRamos, Pablo Ivan PereiraNicolás , Marisa FabianaGuedes, Luciane Prioli CiapinaSilva, Fabrício Alves Barbosa dahttp://lattes.cnpq.br/1823103947133005Colpo, Rodrigo Amarante2023-02-28T19:09:25Z2019-02-25COLPO, R. A. Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes. 2019. 73 f. Dissertação (Programa de Pós-Graduação em Modelagem Computacional) - Laboratório Nacional de Computação Científica, Petrópolis, 2019.https://tede.lncc.br/handle/tede/307Genome-scale metabolic reconstructions are typically created from an annotated genome. However, for not knowing all the functions of the annotated genes, or all the genes in the genome, it is not known all possible reactions that exist in a organism, making metabolic models systematically incomplete. Evidence of this fact is that, if models are created only using an annotated genome, it will be unable to "grow" in conditions where growth was observed in vitro. For the model to "grow" in silico it is necessary to gap fill the model, adding reactions to the model that were not identified during the genome annotation, allowing previously blocked reactions to be activated. This change occurs because the added reactions allowed the substrates of the blocked reactions to be produced, or because they prevented products from the blocked reactions to accumulate inside the network. Genome-scale metabolic models can be semi automatically created using tools that require very little user's experience. However, models created by such tools use metabolites and reactions identifiers that are not compatible with identifiers from KEGG and BiGG Models, what is necessary for the gap filling procedure. Additional problem is the comparison between draft and curated models, since curated models often use BiGG Models identifiers, while automatically created models usually use SEED's. The present work proposes a methodology to translate draft models, from SEED identifiers to BiGG Models identifiers, and translate the KEGG reactions database, from the KEGG identifiers to the BiGG Models identifiers. So that these reactions can be easily used to gap fill the model. Also, based on reactions used by models available in the BiGG Models' database, an interactive menu was created to allow the user to build a customized set of reactions to be used during the gap filling. Finally, all the translated reactions must be stoichiometrically balanced. With the draft model and the reaction dataset created, the gap filling can be performed. The gap filling method developed in this work uses a newly developed method for metabolic network expansion, while more traditional gap filling methods are based on FBA. Therefore, we first studied the necessary conditions for the FBA results and the metabolic networks expansion to be equivalent. With these conditions assured, the method focus on unblocking from the draft model as many original reactions as possible, finding a solution that minimizes the number of reactions added to the model’s final version.Modelos metabólicos em escala genômica são tipicamente criados a partir de um genoma anotado. Entretanto, por não se conhecer todas as funções dos genes anotados, ou por não se conhecer todos os genes do genoma, não se conhecem todas as reações possíveis de existir no organismo, fazendo com que modelos metabólicos sejam sistematicamente incompletos. Evidência deste fato é a incapacidade de modelos de reproduzir in silico o crescimento do organismo observado in vitro, se os modelos forem criados unicamente a partir de um genoma anotado. Para que um modelo possa “crescer”, é necessário realizar o gap filling do modelo, ou seja, acrescentar ao modelo reações que não foram identificadas durante a anotação do genoma, permitindo que reações anteriormente bloqueadas tornem-se capazes de estar ativas. Essa mudança ocorre porque as reações adicionadas permitiram que fossem produzidos os substratos das reações bloqueadas, ou porque evitaram que produtos produzidos pelas reações bloqueadas se acumulassem dentro da rede. Redes metabólicas em escala genômica podem ser construídas utilizando ferramentas que permitem a criação semiautomática de modelos com muito pouca experiência do usuário. Entretanto, os modelos criados por tais ferramentas utilizam nomenclaturas de metabólitos e reações que impossibilitam a utilização de banco de reações do KEGG e do BiGG Models para o procedimento de gap filling da rede metabólica. Dificuldade adicional é a comparação entre os modelos draft e os modelos curados, uma vez que modelos curados costumam utilizar nomenclaturas do BiGG Models, enquanto modelos automáticos costumam utilizar nomenclatura do SEED. O presente trabalho desenvolveu uma ferramenta de tradução de modelos draft, da nomenclatura SEED para a nomenclatura BiGG Models, e traduzir o banco de reações do KEGG, da nomenclatura KEGG para a nomenclatura BiGG Models, para que as suas reações possam ser utilizadas para o gap filling da rede metabólica. Além disso, desenvolveu-se menu interativo para que o usuário possa criar um conjunto personalizado de reações a ser utilizado durante o gap filling, tendo por base as reações utilizadas por modelos disponibilizados no BiGG Models. Por fim, todas as reações do modelo traduzido e do conjunto de reações a ser utilizado durante o gap filling são estequiometricamente balanceadas, a fim de que não haja fluxo em reações quando não é disponibilizado reações que simulem um meio de cultura. Com o modelo draft e o banco de reações traduzidas e estequiometricamente balanceadas, foi realizado o gap filling do modelo. Ao contrário dos métodos de gap filling mais tradicionais, que são baseados em flux balance analysis (FBA), aqui se utilizou o método de expansão de redes metabólicas (ERM). Por isso, primeiro se estudou as condições necessárias para que os resultados de FBA e ERM fossem equivalentes. Com essas condições asseguradas, o método não se concentra em desbloquear apenas uma única reação objetivo, mas em desbloquear o maior número possível de reações originais do modelo draft, encontrando uma solução ótima que minimiza o número de reações adicionadas à versão final do modelo.Submitted by Parícia Vieira Silva (library@lncc.br) on 2023-02-28T19:05:32Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Rodrigo Colpo_Dissertacao 2019.pdf: 2429064 bytes, checksum: c96119a6d14bb5ca5b05ffe88c35a55b (MD5)Approved for entry into archive by Parícia Vieira Silva (library@lncc.br) on 2023-02-28T19:08:11Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Rodrigo Colpo_Dissertacao 2019.pdf: 2429064 bytes, checksum: c96119a6d14bb5ca5b05ffe88c35a55b (MD5)Made available in DSpace on 2023-02-28T19:09:25Z (GMT). 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dc.title.por.fl_str_mv |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes |
title |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes |
spellingShingle |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes Colpo, Rodrigo Amarante Bioinformática Simulação (Computadores) Gap Filling CNPQ::CIENCIAS BIOLOGICAS::GENETICA |
title_short |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes |
title_full |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes |
title_fullStr |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes |
title_full_unstemmed |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes |
title_sort |
Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes |
author |
Colpo, Rodrigo Amarante |
author_facet |
Colpo, Rodrigo Amarante |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Nicolás, Marisa Fabiana |
dc.contributor.advisor2.fl_str_mv |
Ramos, Pablo Ivan Pereira |
dc.contributor.referee1.fl_str_mv |
Nicolás , Marisa Fabiana |
dc.contributor.referee2.fl_str_mv |
Guedes, Luciane Prioli Ciapina |
dc.contributor.referee3.fl_str_mv |
Silva, Fabrício Alves Barbosa da |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1823103947133005 |
dc.contributor.author.fl_str_mv |
Colpo, Rodrigo Amarante |
contributor_str_mv |
Nicolás, Marisa Fabiana Ramos, Pablo Ivan Pereira Nicolás , Marisa Fabiana Guedes, Luciane Prioli Ciapina Silva, Fabrício Alves Barbosa da |
dc.subject.por.fl_str_mv |
Bioinformática Simulação (Computadores) |
topic |
Bioinformática Simulação (Computadores) Gap Filling CNPQ::CIENCIAS BIOLOGICAS::GENETICA |
dc.subject.eng.fl_str_mv |
Gap Filling |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS BIOLOGICAS::GENETICA |
description |
Genome-scale metabolic reconstructions are typically created from an annotated genome. However, for not knowing all the functions of the annotated genes, or all the genes in the genome, it is not known all possible reactions that exist in a organism, making metabolic models systematically incomplete. Evidence of this fact is that, if models are created only using an annotated genome, it will be unable to "grow" in conditions where growth was observed in vitro. For the model to "grow" in silico it is necessary to gap fill the model, adding reactions to the model that were not identified during the genome annotation, allowing previously blocked reactions to be activated. This change occurs because the added reactions allowed the substrates of the blocked reactions to be produced, or because they prevented products from the blocked reactions to accumulate inside the network. Genome-scale metabolic models can be semi automatically created using tools that require very little user's experience. However, models created by such tools use metabolites and reactions identifiers that are not compatible with identifiers from KEGG and BiGG Models, what is necessary for the gap filling procedure. Additional problem is the comparison between draft and curated models, since curated models often use BiGG Models identifiers, while automatically created models usually use SEED's. The present work proposes a methodology to translate draft models, from SEED identifiers to BiGG Models identifiers, and translate the KEGG reactions database, from the KEGG identifiers to the BiGG Models identifiers. So that these reactions can be easily used to gap fill the model. Also, based on reactions used by models available in the BiGG Models' database, an interactive menu was created to allow the user to build a customized set of reactions to be used during the gap filling. Finally, all the translated reactions must be stoichiometrically balanced. With the draft model and the reaction dataset created, the gap filling can be performed. The gap filling method developed in this work uses a newly developed method for metabolic network expansion, while more traditional gap filling methods are based on FBA. Therefore, we first studied the necessary conditions for the FBA results and the metabolic networks expansion to be equivalent. With these conditions assured, the method focus on unblocking from the draft model as many original reactions as possible, finding a solution that minimizes the number of reactions added to the model’s final version. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019-02-25 |
dc.date.accessioned.fl_str_mv |
2023-02-28T19:09:25Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
COLPO, R. A. Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes. 2019. 73 f. Dissertação (Programa de Pós-Graduação em Modelagem Computacional) - Laboratório Nacional de Computação Científica, Petrópolis, 2019. |
dc.identifier.uri.fl_str_mv |
https://tede.lncc.br/handle/tede/307 |
identifier_str_mv |
COLPO, R. A. Um novo algoritmo de gap filling de rede metabólica aplicando estratégia de expansão de redes. 2019. 73 f. Dissertação (Programa de Pós-Graduação em Modelagem Computacional) - Laboratório Nacional de Computação Científica, Petrópolis, 2019. |
url |
https://tede.lncc.br/handle/tede/307 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Laboratório Nacional de Computação Científica |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Modelagem Computacional |
dc.publisher.initials.fl_str_mv |
LNCC |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Coordenação de Pós-Graduação e Aperfeiçoamento (COPGA) |
publisher.none.fl_str_mv |
Laboratório Nacional de Computação Científica |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do LNCC instname:Laboratório Nacional de Computação Científica (LNCC) instacron:LNCC |
instname_str |
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instacron_str |
LNCC |
institution |
LNCC |
reponame_str |
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collection |
Biblioteca Digital de Teses e Dissertações do LNCC |
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