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...
- 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 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
dc.type.content.spa.fl_str_mv |
Text |
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http://purl.org/redcol/resource_type/TM |
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 |
dc.identifier.repourl.spa.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
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http://hdl.handle.net/1992/34156 |
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u806870.pdf instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
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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info:eu-repo/semantics/openAccess |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.es_CO.fl_str_mv |
29 hojas |
dc.format.mimetype.es_CO.fl_str_mv |
application/pdf |
dc.publisher.es_CO.fl_str_mv |
Uniandes |
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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instname:Universidad de los Andes reponame:Repositorio Institucional Séneca |
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Universidad de los Andes |
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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 |