Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente

Las industrias de curtiembres son el sector industrial con mayor impacto en la contaminación de metales pesados en el río Bogotá por sus vertimientos de altas concentraciones de cromo hexavalente. Este contaminante tiene consecuencias mutagénicas con repercusiones en la salud de los diferentes entes...

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Autores:: Osorio Bautista, Juan Sebastián

Tipo de recurso:: https://purl.org/coar/resource_type/c_7a1f

Fecha de publicación:: 2024

Institución:: Universidad El Bosque

Repositorio:: Repositorio U. El Bosque

Idioma:: spa

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network_acronym_str	UNBOSQUE2
network_name_str	Repositorio U. El Bosque
repository_id_str
dc.title.none.fl_str_mv	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente
dc.title.translated.none.fl_str_mv	Development of a predictive algorithm for bacterial consortia based on metabolic characteristics with potential use for the bioremediation of wastewater contaminated with hexavalent chromium
title	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente
spellingShingle	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente Aprendizaje automático Abundancia taxonómica Biorremediación Consorcio bacteriano Cromo hexavalente Perfiles funcionales 610.28 Machine learning Taxonomic abundance Bioremediation Bacteria consortia Hexavalent chromium Function profiles
title_short	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente
title_full	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente
title_fullStr	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente
title_full_unstemmed	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente
title_sort	Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente
dc.creator.fl_str_mv	Osorio Bautista, Juan Sebastián
dc.contributor.advisor.none.fl_str_mv	Torres Ballesteros, Adriana María Merchán Castellanos, Nuri Andrea
dc.contributor.author.none.fl_str_mv	Osorio Bautista, Juan Sebastián
dc.subject.none.fl_str_mv	Aprendizaje automático Abundancia taxonómica Biorremediación Consorcio bacteriano Cromo hexavalente Perfiles funcionales
topic	Aprendizaje automático Abundancia taxonómica Biorremediación Consorcio bacteriano Cromo hexavalente Perfiles funcionales 610.28 Machine learning Taxonomic abundance Bioremediation Bacteria consortia Hexavalent chromium Function profiles
dc.subject.ddc.none.fl_str_mv	610.28
dc.subject.keywords.none.fl_str_mv	Machine learning Taxonomic abundance Bioremediation Bacteria consortia Hexavalent chromium Function profiles
description	Las industrias de curtiembres son el sector industrial con mayor impacto en la contaminación de metales pesados en el río Bogotá por sus vertimientos de altas concentraciones de cromo hexavalente. Este contaminante tiene consecuencias mutagénicas con repercusiones en la salud de los diferentes entes biológicos presentes en el ecosistema hídrico, como lo son los microorganismos. Se han propuesto técnicas para la remoción del contaminante que han sido un desafío para su desarrollo e implementación. La biorremediación se presenta como alternativa sostenible para la remoción de este metal pesado, pero su desarrollo depende de factores esenciales como la selección de microorganismos con potencial de biorremediación. El objetivo de este trabajo fue desarrollar un algoritmo predictor de consorcios bacterianos, basado en la información obtenida de rutas metabólicas, perfiles taxonómicos y perfiles funcionales de las bacterias presentes en el río Bogotá, con potencial uso para la biorremediación de Cromo hexavalente. Se identificaron los perfiles taxonómicos y funcionales de las bacterias presentes en tres puntos de muestreo del río Bogotá mediante secuenciamiento del gen 16s. El análisis bioinformático no presentó una alfa diversidad significativa con un rango de 2.75 a 3.50 (índice de Shannon). La beta diversidad evidenció cambios significativos en la composición bacteriana de los puntos (0,76 distancia de Bray Curtis). Se identificó un aumento en la riqueza de especies Acinetobacter johnsonii y Pseudomonas aeruginosa, así como dominancia de los géneros Arthrobacter, Duganella, Flavobacterium, Limnohabitans y Rhodoferax. Se implementó el modelo de regresión Random Forest para la predicción del porcentaje de biorremediación capaz de realizar cada perfil taxonómico. Este modelo expuso un error cuadrático de medio porcentaje de biorremediación de 5,41. Se implementó adicionalmente el método de clusterización aglomerativo jerárquico Agnes para la conformación del consorcio bacteriano biorremediador. El algoritmo utilizó la información de conectividad detectada en la red ecológica microbiana, los perfiles funcionales y la capacidad de biorremediación de 40 bacterias identificadas como indicadoras del aumento de la concentración de cromo (VI) en el río Bogotá. El algoritmo agrupó las bacterias Pseudomonas peli, Pseudomonas aeruginosa, Burkholderia singularis, Acinetobacter Bahumannii y Dechloromonas denitrificans en el cluster con mayor capacidad de biorremediación de Cr VI (92,2%). La capacidad de biorremediación del consorcio natural obtenido por método de extinción y el consorcio predicho por el algoritmo fue comparada, evidenciando un 78,14% y 87.19% de remoción respectivamente.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-09T16:24:00Z
dc.date.available.none.fl_str_mv	2024-07-09T16:24:00Z
dc.date.issued.none.fl_str_mv	2024-05
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.local.spa.fl_str_mv	Tesis/Trabajo de grado - Monografía - Pregrado
dc.type.coar.none.fl_str_mv	https://purl.org/coar/resource_type/c_7a1f
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/bachelorThesis
dc.type.coarversion.none.fl_str_mv	https://purl.org/coar/version/c_970fb48d4fbd8a85
format	https://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12495/12591
dc.identifier.instname.spa.fl_str_mv	instname:Universidad El Bosque
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad El Bosque
dc.identifier.repourl.none.fl_str_mv	https://repositorio.unbosque.edu.co
url	https://hdl.handle.net/20.500.12495/12591 https://repositorio.unbosque.edu.co
identifier_str_mv	instname:Universidad El Bosque reponame:Repositorio Institucional Universidad El Bosque
dc.language.iso.fl_str_mv	spa
language	spa
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spelling	Torres Ballesteros, Adriana MaríaMerchán Castellanos, Nuri AndreaOsorio Bautista, Juan Sebastián2024-07-09T16:24:00Z2024-07-09T16:24:00Z2024-05https://hdl.handle.net/20.500.12495/12591instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquehttps://repositorio.unbosque.edu.coLas industrias de curtiembres son el sector industrial con mayor impacto en la contaminación de metales pesados en el río Bogotá por sus vertimientos de altas concentraciones de cromo hexavalente. Este contaminante tiene consecuencias mutagénicas con repercusiones en la salud de los diferentes entes biológicos presentes en el ecosistema hídrico, como lo son los microorganismos. Se han propuesto técnicas para la remoción del contaminante que han sido un desafío para su desarrollo e implementación. La biorremediación se presenta como alternativa sostenible para la remoción de este metal pesado, pero su desarrollo depende de factores esenciales como la selección de microorganismos con potencial de biorremediación. El objetivo de este trabajo fue desarrollar un algoritmo predictor de consorcios bacterianos, basado en la información obtenida de rutas metabólicas, perfiles taxonómicos y perfiles funcionales de las bacterias presentes en el río Bogotá, con potencial uso para la biorremediación de Cromo hexavalente. Se identificaron los perfiles taxonómicos y funcionales de las bacterias presentes en tres puntos de muestreo del río Bogotá mediante secuenciamiento del gen 16s. El análisis bioinformático no presentó una alfa diversidad significativa con un rango de 2.75 a 3.50 (índice de Shannon). La beta diversidad evidenció cambios significativos en la composición bacteriana de los puntos (0,76 distancia de Bray Curtis). Se identificó un aumento en la riqueza de especies Acinetobacter johnsonii y Pseudomonas aeruginosa, así como dominancia de los géneros Arthrobacter, Duganella, Flavobacterium, Limnohabitans y Rhodoferax. Se implementó el modelo de regresión Random Forest para la predicción del porcentaje de biorremediación capaz de realizar cada perfil taxonómico. Este modelo expuso un error cuadrático de medio porcentaje de biorremediación de 5,41. Se implementó adicionalmente el método de clusterización aglomerativo jerárquico Agnes para la conformación del consorcio bacteriano biorremediador. El algoritmo utilizó la información de conectividad detectada en la red ecológica microbiana, los perfiles funcionales y la capacidad de biorremediación de 40 bacterias identificadas como indicadoras del aumento de la concentración de cromo (VI) en el río Bogotá. El algoritmo agrupó las bacterias Pseudomonas peli, Pseudomonas aeruginosa, Burkholderia singularis, Acinetobacter Bahumannii y Dechloromonas denitrificans en el cluster con mayor capacidad de biorremediación de Cr VI (92,2%). La capacidad de biorremediación del consorcio natural obtenido por método de extinción y el consorcio predicho por el algoritmo fue comparada, evidenciando un 78,14% y 87.19% de remoción respectivamente.BioingenieroPregradoThe tannery industries are the industrial sector with the greatest impact on heavy metal pollution in the Bogotá River due to their discharge of high concentrations of hexavalent chromium. This pollutant has mutagenic consequences with repercussions on the health of different biological entities present in the aquatic ecosystem, such as microorganisms. Techniques for removing the pollutant have been proposed, which have been challenging for their development and implementation. Bioremediation emerges as a sustainable alternative for the removal of this heavy metal, but its development depends on essential factors such as the selection of microorganisms with bioremediation potential. The objective of this work was to develop a predictive algorithm of bacterial consortia, based on information obtained from metabolic pathways, taxonomic profiles, and functional profiles of bacteria present in the Bogotá River, with potential use for the bioremediation of hexavalent chromium. Taxonomic and functional profiles of bacteria present at three sampling points along the Bogotá River were identified using 16s gene sequencing. Bioinformatic analysis did not show significant alpha diversity with a range from 2.75 to 3.50 (Shannon index). Beta diversity revealed significant changes in bacterial composition between the points (Bray Curtis distance of 0.76). An increase in species richness of Acinetobacter johnsonii and Pseudomonas aeruginosa was identified, as well as dominance of the genera Arthrobacter, Duganella, Flavobacterium, Limnohabitans, and Rhodoferax. The Random Forest regression model was implemented for predicting the percentage of bioremediation achievable by each taxonomic profile. This model exhibited a mean squared error of 5.41 for bioremediation percentage. Additionally, the hierarchical agglomerative clustering method Agnes was implemented for the formation of the bioremediation bacterial consortium. The algorithm used connectivity information detected in the microbial ecological network, functional profiles, and the bioremediation capacity of 40 bacteria identified as indicators of increased chromium (VI) concentration in the Bogotá River. The algorithm grouped the bacteria Pseudomonas peli, Pseudomonas aeruginosa, Burkholderia singularis, Acinetobacter Bahumannii, and Dechloromonas denitrificans in the cluster with the highest Cr VI bioremediation capacity (92.2%). The bioremediation capacity of the naturally obtained consortium by extinction method and the consortium predicted by the algorithm was compared, showing 78.14% and 87.19% removal, respectively.application/pdfAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Acceso abiertoinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aprendizaje automáticoAbundancia taxonómicaBiorremediaciónConsorcio bacterianoCromo hexavalentePerfiles funcionales610.28Machine learningTaxonomic abundanceBioremediationBacteria consortiaHexavalent chromiumFunction profilesDesarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalenteDevelopment of a predictive algorithm for bacterial consortia based on metabolic characteristics with potential use for the bioremediation of wastewater contaminated with hexavalent chromiumBioingenieríaUniversidad El BosqueFacultad de IngenieríaTesis/Trabajo de grado - Monografía - Pregradohttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_970fb48d4fbd8a85Aguilar, C. 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Desarrollo de un algoritmo predictor de consorcios bacterianos, partiendo de características metabólicas con potencial uso para la biorremediación de aguas residuales contaminadas con cromo hexavalente

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