Metabolic network reconstruction of Manihot esculenta Crantz

The global harvested area of cassava (Manihot esculenta Crantz) is 19.6 (2011) million hectares. Cassava grows throughout the tropic and it is cultivated by small-scale farmers in areas where soils are poor and rainfall is low. It has one of the biggest harvested area increase among the world's...

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Autores:
Gómez Cano, Fabio Andrés
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/34156
Acceso en línea:
http://hdl.handle.net/1992/34156
Palabra clave:
Yuca - Cultivo - Investigaciones
Biomasa - Investigaciones
Euforbiáceas - Investigaciones
Biología
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
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dc.title.es_CO.fl_str_mv Metabolic network reconstruction of Manihot esculenta Crantz
title Metabolic network reconstruction of Manihot esculenta Crantz
spellingShingle Metabolic network reconstruction of Manihot esculenta Crantz
Yuca - Cultivo - Investigaciones
Biomasa - Investigaciones
Euforbiáceas - Investigaciones
Biología
title_short Metabolic network reconstruction of Manihot esculenta Crantz
title_full Metabolic network reconstruction of Manihot esculenta Crantz
title_fullStr Metabolic network reconstruction of Manihot esculenta Crantz
title_full_unstemmed Metabolic network reconstruction of Manihot esculenta Crantz
title_sort Metabolic network reconstruction of Manihot esculenta Crantz
dc.creator.fl_str_mv Gómez Cano, Fabio Andrés
dc.contributor.advisor.none.fl_str_mv Bernal Giraldo, Adriana Jimena
López Carascal, Camilo Ernesto
dc.contributor.author.none.fl_str_mv Gómez Cano, Fabio Andrés
dc.contributor.jury.none.fl_str_mv López Kleine, Liliana
Zimmermann, Barbara Hanna
dc.subject.keyword.es_CO.fl_str_mv Yuca - Cultivo - Investigaciones
Biomasa - Investigaciones
Euforbiáceas - Investigaciones
topic Yuca - Cultivo - Investigaciones
Biomasa - Investigaciones
Euforbiáceas - Investigaciones
Biología
dc.subject.themes.none.fl_str_mv Biología
description The global harvested area of cassava (Manihot esculenta Crantz) is 19.6 (2011) million hectares. Cassava grows throughout the tropic and it is cultivated by small-scale farmers in areas where soils are poor and rainfall is low. It has one of the biggest harvested area increase among the world's food crops (44 %) in the last two decades. Herein, we generate a compartmentalized genome-scale model for cassava, which takes into account the gene- protein-reaction (GPR) relationships. The stoichiometric values of each reaction were assigned according with Metanetx annotation. The Gibbs free changes of reactions (ArG') was used to predict reaction directionality. Compartmentalization was carried out using a multiple gene-location prediction analysis followed by a cluster analysis. To assay the network gaps, gapfin/gapfill algorithm were used, along with topological metrics. The model was assayed using flux balance analysis (FBA). Optimization was carried out using biomass production as the objective function. In total, 8526 genes, 5253 metabolites and 4636 reactions associated to primary and secondary metabolism were identified. All reactions were localized into five different compartments (i.e., cytoplasm, mitochondrion, plastid, vacuole and extracellular). The FBA under normal and modified condition (i.g., deletions of biomass components) identified that the hemicellulose is a major biomass contributor in cassava. In addition, the importance of cyanogenic compound conversion reactions in connection with biomass production was evidenced, potentially through the release of beta- D-glucose and D-glucose. This model is the first cassava metabolic model and the first model among the Euphorbiaceae family. In addition, we present an gene sequence-based method for the compartmentalization of metabolic models based on genomic data.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2020-06-10T08:58:46Z
dc.date.available.none.fl_str_mv 2020-06-10T08:58:46Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
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dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/1992/34156
dc.identifier.pdf.none.fl_str_mv u806870.pdf
dc.identifier.instname.spa.fl_str_mv instname:Universidad de los Andes
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Séneca
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url http://hdl.handle.net/1992/34156
identifier_str_mv u806870.pdf
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reponame:Repositorio Institucional Séneca
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dc.language.iso.es_CO.fl_str_mv eng
language eng
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
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dc.format.extent.es_CO.fl_str_mv 29 hojas
dc.format.mimetype.es_CO.fl_str_mv application/pdf
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dc.publisher.program.es_CO.fl_str_mv Maestría en Ciencias Biológicas
dc.publisher.faculty.es_CO.fl_str_mv Facultad de Ciencias
dc.publisher.department.es_CO.fl_str_mv Departamento de Biología
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spelling Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bernal Giraldo, Adriana Jimena721a9490-6cc8-4365-b58a-154fb21deb4a400López Carascal, Camilo Ernesto9c9e6b0a-e8b6-4f83-8857-167dc17e5328500Gómez Cano, Fabio Andrés45c86fb0-51f6-4cfb-8b64-9dfa7cc74afb500López Kleine, LilianaZimmermann, Barbara Hanna2020-06-10T08:58:46Z2020-06-10T08:58:46Z2017http://hdl.handle.net/1992/34156u806870.pdfinstname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The global harvested area of cassava (Manihot esculenta Crantz) is 19.6 (2011) million hectares. Cassava grows throughout the tropic and it is cultivated by small-scale farmers in areas where soils are poor and rainfall is low. It has one of the biggest harvested area increase among the world's food crops (44 %) in the last two decades. Herein, we generate a compartmentalized genome-scale model for cassava, which takes into account the gene- protein-reaction (GPR) relationships. The stoichiometric values of each reaction were assigned according with Metanetx annotation. The Gibbs free changes of reactions (ArG') was used to predict reaction directionality. Compartmentalization was carried out using a multiple gene-location prediction analysis followed by a cluster analysis. To assay the network gaps, gapfin/gapfill algorithm were used, along with topological metrics. The model was assayed using flux balance analysis (FBA). Optimization was carried out using biomass production as the objective function. In total, 8526 genes, 5253 metabolites and 4636 reactions associated to primary and secondary metabolism were identified. All reactions were localized into five different compartments (i.e., cytoplasm, mitochondrion, plastid, vacuole and extracellular). The FBA under normal and modified condition (i.g., deletions of biomass components) identified that the hemicellulose is a major biomass contributor in cassava. In addition, the importance of cyanogenic compound conversion reactions in connection with biomass production was evidenced, potentially through the release of beta- D-glucose and D-glucose. This model is the first cassava metabolic model and the first model among the Euphorbiaceae family. In addition, we present an gene sequence-based method for the compartmentalization of metabolic models based on genomic data."El área cosechada a nivel mundial de yuca (Manihot esculenta Crantz) es de 19.6 (2011) millones de hectáreas. La yuca crece en todo el trópico y es cultivada por pequeños agricultores en áreas donde los suelos son pobres y la lluvia es baja. Tiene uno de los mayores incrementos de área cosechada entre los cultivos alimentarios del mundo (44%) en las últimas dos décadas. En este trabajo, generamos un modelo de compartimentalizado a nivel genómico para la yuca, que tiene en cuenta las relaciones de gene-proteína-reacción (GPR). Los valores estequiométricos de cada reacción se asignaron de acuerdo con la anotación Metanetx. Los cambios de reacciones libres de Gibbs (ArG') se usaron para predecir la direccionalidad de las reacciones. La compartimentalización se llevó a cabo utilizando un análisis con múltiples predicciones seguido de un análisis de agrupamiento. Para evaluar los vacios de la red, se utilizó el algoritmo gapfin / gapfill, junto con métricas topológicas. El modelo se evaluó usando el análisis de balance de flujo (FBA). La optimización se llevó a cabo utilizando la producción de biomasa como la función objetivo. En total, se identificaron 8526 genes, 5253 metabolitos y 4636 reacciones asociadas al metabolismo primario y secundario. Todas las reacciones se localizaron en cinco compartimentos diferentes (citoplasma, mitocondria, plástido, vacuola y extracelular). El FBA en condiciones normales y modificadas (por ejemplo, eliminaciones de componentes de biomasa) identificó que la hemicelulosa es un importante contribuyente de biomasa en la yuca. Además, se evidenció la importancia de las reacciones de conversión de compuestos cianogénicos en relación con la producción de biomasa, potencialmente a través de la liberación de beta-D-glucosa y D-glucosa. Este modelo es el primer modelo metabólico de la yuca y el primer modelo entre la familia Euphorbiaceae. Además, presentamos un método alternativo para la compartimentación de modelos metabólicos basados en dato"--Tomado del Formato de Documento de Grado.Magíster en Ciencias BiológicasMaestría29 hojasapplication/pdfengUniandesMaestría en Ciencias BiológicasFacultad de CienciasDepartamento de Biologíainstname:Universidad de los Andesreponame:Repositorio Institucional SénecaMetabolic network reconstruction of Manihot esculenta CrantzTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TMYuca - Cultivo - InvestigacionesBiomasa - InvestigacionesEuforbiáceas - InvestigacionesBiologíaPublicationTHUMBNAILu806870.pdf.jpgu806870.pdf.jpgIM Thumbnailimage/jpeg4919https://repositorio.uniandes.edu.co/bitstreams/83447961-5681-40b8-9893-80969b7c02b5/download97b43e6007a8867d5b3a9ab0a5502beaMD55TEXTu806870.pdf.txtu806870.pdf.txtExtracted texttext/plain67955https://repositorio.uniandes.edu.co/bitstreams/1e37b042-3a19-47e6-986a-964acb5e7946/download660ceda1248af31f318710055c60b9c3MD54ORIGINALu806870.pdfapplication/pdf3113745https://repositorio.uniandes.edu.co/bitstreams/caa4d090-0b21-4f96-86f2-8ef3ee1850f8/download319839370d1eefe1468e3acd9e673ceaMD511992/34156oai:repositorio.uniandes.edu.co:1992/341562023-10-10 19:48:29.169http://creativecommons.org/licenses/by-nc-sa/4.0/open.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co