Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson

ilustraciones, gráficas, tablas

Autores:
Forero Rodriguez, Lady Johanna
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/84412
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/84412
https://repositorio.unal.edu.co/
Palabra clave:
610 - Medicina y salud::616 - Enfermedades
600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados
Parkinson Disease/pathology
Gastrointestinal Contents
Models, Theoretical
Contenido Digestivo
Enfermedad de Parkinson/patología
Modelos Teóricos
Enfermedad de Parkinson
Microbioma intestinal
Modelamiento computacional
Modelamiento metabólico
Dieta
Parkinson's disease
Diet
Gut microbiome
Computational modeling
Metabolic modeling
Rights
openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_7506ae3094980b1b01a571532caa7ce4
oai_identifier_str oai:repositorio.unal.edu.co:unal/84412
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
dc.title.translated.eng.fl_str_mv Computational modeling of intestinal bacterial composition in the context of Parkinson's disease
title Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
spellingShingle Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
610 - Medicina y salud::616 - Enfermedades
600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados
Parkinson Disease/pathology
Gastrointestinal Contents
Models, Theoretical
Contenido Digestivo
Enfermedad de Parkinson/patología
Modelos Teóricos
Enfermedad de Parkinson
Microbioma intestinal
Modelamiento computacional
Modelamiento metabólico
Dieta
Parkinson's disease
Diet
Gut microbiome
Computational modeling
Metabolic modeling
title_short Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
title_full Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
title_fullStr Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
title_full_unstemmed Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
title_sort Modelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de Parkinson
dc.creator.fl_str_mv Forero Rodriguez, Lady Johanna
dc.contributor.advisor.spa.fl_str_mv Pinzón Velasco, Andrés Mauricio
dc.contributor.author.spa.fl_str_mv Forero Rodriguez, Lady Johanna
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación en Bioinformática y Biología de Sistemas
dc.contributor.researchgate.spa.fl_str_mv https://www.researchgate.net/profile/Lady-Forero-Rodriguez
dc.subject.ddc.spa.fl_str_mv 610 - Medicina y salud::616 - Enfermedades
600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados
topic 610 - Medicina y salud::616 - Enfermedades
600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados
Parkinson Disease/pathology
Gastrointestinal Contents
Models, Theoretical
Contenido Digestivo
Enfermedad de Parkinson/patología
Modelos Teóricos
Enfermedad de Parkinson
Microbioma intestinal
Modelamiento computacional
Modelamiento metabólico
Dieta
Parkinson's disease
Diet
Gut microbiome
Computational modeling
Metabolic modeling
dc.subject.decs.eng.fl_str_mv Parkinson Disease/pathology
Gastrointestinal Contents
Models, Theoretical
dc.subject.decs.spa.fl_str_mv Contenido Digestivo
Enfermedad de Parkinson/patología
Modelos Teóricos
dc.subject.proposal.spa.fl_str_mv Enfermedad de Parkinson
Microbioma intestinal
Modelamiento computacional
Modelamiento metabólico
Dieta
dc.subject.proposal.eng.fl_str_mv Parkinson's disease
Diet
Gut microbiome
Computational modeling
Metabolic modeling
description ilustraciones, gráficas, tablas
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-02T02:31:27Z
dc.date.available.none.fl_str_mv 2023-08-02T02:31:27Z
dc.date.issued.none.fl_str_mv 2023
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/84412
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/84412
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.indexed.spa.fl_str_mv Bireme
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spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pinzón Velasco, Andrés Mauricio366c2eddf6aa24434ee55e57da235448Forero Rodriguez, Lady Johanna56439fd19ae816d16bd06a82c54a76efGrupo de Investigación en Bioinformática y Biología de Sistemashttps://www.researchgate.net/profile/Lady-Forero-Rodriguez2023-08-02T02:31:27Z2023-08-02T02:31:27Z2023https://repositorio.unal.edu.co/handle/unal/84412Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLa Enfermedad de Parkinson (EP) es una enfermedad neurodegenerativa de carácter progresivo y crónico, principal causa de pérdida de coordinación de los movimientos (alteraciones motoras) y de alteraciones no motoras, entre las que se incluyen los síntomas gastrointestinales. Clínicamente es definida como la pérdida de neuronas dopaminérgicas en el mesencéfalo. Alteraciones características en la composición de la microbiota intestinal han sido reportadas en pacientes con EP vs controles sanos, sin embargo no se ha caracterizado la composición en pacientes vs controles en latinoamérica y su función aún no es clara en el contexto de esta enfermedad. Para abordar dicha problemática se obtuvo materia fecal de participantes Colombianos (n =25 EP casos idiopáticos, n =25 controles) para el análisis de información de 16S rRNA y su posterior modelado computacional. Los participantes con EP fueron evaluados por un neurólogo experto en desórdenes del movimiento y todos los individuos respondieron a cuestionarios de consumo. Las muestras de heces de estos individuos fueron analizadas a través de la secuenciación del gen 16S rRNA. Se hizo análisis de diversidad, composición diferencial y modelamiento computacional individualizado teniendo en cuenta la dieta y composición bacteriana fecal de cada participante. Nuestros resultados más importantes incluyen lo siguiente: tres metabolitos de la dieta fueron diferentes entre pacientes con PD y controles, tales como Ácidos grasos trans, Carbohidratos y Potasio. Seis zOTUs cambiaron significativamente en sus abundancias relativas entre pacientes con EP ycontroles sanos. Familias Verrucomicrobioaceae, Lachnospiraceae, Peptostreptococcaceae, Lactobacilliaceae, y Streptococcaceae. Además, el modelado metabólico personalizado de los microbiomas intestinales reveló patrones metabólicos que pueden asociarse a la enfermedad, particularmente la particularmente la producción de cinco metabolitos (Ácido fenilacético , Indol , L-triptófano, D-Fructosa y Ácido Mirístico), relacionados al metabolismo de aminoácidos aromáticos y la dieta. Por tanto, los resultados sugieren que la dieta y la composición bacteriana intestinal podrían afectar el metabolismo del huésped y así mismo relacionarse con la enfermedad. (Texto tomado de la fuente).Parkinson's Disease (PD) is a progressive and chronic neurodegenerative disease that is the main cause of loss of coordination of movements (motor disorders) and non-motor disorders, including gastrointestinal symptoms. Clinically it is defined as the loss of dopaminergic neurons in the midbrain. Alterations in the composition of the intestinal microbiota have been reported in patients with PD vs healthy controls, however the composition in patients vs. controls in Latin America has not been characterized and its function is still unclear in the context of this disease. To address this problem, fecal matter was obtained from Colombian participants (n = 25 PD idiopathic cases, n = 25 controls) for the analysis of rRNA16S information. Participants with PD were evaluated by a neurologist with expertise in movement disorders, and all individuals answered consumer questionnaires. All this formation was therefore used for further personalized computational analysis. Stool samples from these individuals were analyzed through 16S rRNA gene sequencing. An analysis of diversity, differential composition and individualized computational modeling was carried out, taking into account the diet and fecal bacterial composition of each participant. Our most important results include the following: three dietary metabolites were different between PD patients and controls such as Trans Fatty Acids, Carbohydrates and Potassium. Six zOTUs changed significantly in their relative abundances between PD patients and healthy controls. Families Verrucomicrobioaceae, Lachnospiraceae, Peptostreptococcaceae, Lactobacilliaceae, and Streptococcaceae. Furthermore, individualized metabolic modeling of gut microbiomes revealed metabolic patterns that may be associated with disease, production of five metabolites (Phenylacetic Acid, Indole, L-Tryptophan, D-Fructose, and Myristic Acid), related to the metabolism of aromatic amino acids, and the diet. Therefore, the results suggest that the diet and the intestinal bacterial composition could affect the metabolism of the host and therefore relate to the disease.DoctoradoDoctor en Ciencias BiomédicasSujetos de estudio Originalmente se reclutaron 56 sujetos de edad (con una diferencia máxima de + 2 años) y sexo emparejados (31 pacientes con EP, 25 sujetos de control) para participar en el estudio. Se excluyeron 6 participantes en base a los criterios de inclusión/exclusión, con lo que el número total de sujetos fue de 50 (25 pacientes con EP, 25 sujetos de control). El estudio fue aprobado por el comité de ética de la Universidad Nacional de Colombia y todos los participantes dieron su consentimiento informado (Anexo A). El diagnóstico de los pacientes fue realizado por un neurólogo especialista con amplia experiencia en trastornos del movimiento, utilizando los datos obtenidos de historias clínicas y la escala de deterioro de Webster para la enfermedad de Parkinson, la cual cuantifica el grado de inhabilidad de un paciente, determinando así la severidad de la enfermedad. Los criterios de exclusión utilizados fueron: 1) uso regular de probióticos o antibióticos los últimos 3 meses antes de la toma de la muestra. 2) Parkinsonismo secundario, 3) Parkinson familiar, 3) Parkinson familiar, 3) enfermedades primarias gastrointestinales, 4) otras alteraciones neurológicas o psiquiátricas, 5) cambios en los hábitos dietarios. Para los controles los criterios de exclusión fueron 1) uso regular de probióticos o antibióticos los últimos 3 meses antes de la toma de muestras. 2)50 enfermedades primarias gastrointestinales, 3) alteraciones neurológicas o psiquiátricas, 4) cambios en los hábitos dietarios. Toma de muestras y extracción de ADN Las muestras fecales se recogieron en casa con las indicaciones del médico en recipientes estériles desechables. Las muestras fecales se recogieron en horas de la mañana, se congelaron y se almacenaron a -20°C para su posterior procesamiento (máximo 3 días después). Se utilizó el ZymoBIOMICS DNA Miniprep Kit (Zymo Research) según las instrucciones del fabricante y ciertos perfeccionamientos del protocolo en casa (Anexo 2). Tras la extracción del ADN (3 réplicas técnicas por muestra), se cuantificó mediante Nanodrop ND-1000 (Thermo Fisher Scientific, Wilmington, DE), Qubit Invitrogen (Life Technologies, CA) y se evaluó la integridad del ADN mediante electroforesis en gel de agarosa al 0,8%. Amplicón del gen 16S rRNA y secuenciación Los extractos de ADN se transfirieron a placas de 96 pocillos. Los servicios de secuenciación de la Universidad de Iowa (Iowa State University DNA Facility, Estados Unidos) se encargaron de la preparación y secuenciación del ADN. Las regiones hipervariables del gen 16S rRNA bacteriano V4-V5 se amplificaron utilizando el cebador universal 16S forward (515F: GTGYCAGCMGCCGCGTAA) y el cebador reverse (926R:CCGYCAATTYMTTTRAGTTT). De este modo, las bibliotecas de ADN se multiplexaron para su secuenciación en la plataforma Illumina MiSeq (2 × 250 fines pareados) en un solo carril de la celda de flujo. Bioinformática y análisis estadístico Se obtuvieron los datos ya demultiplexados, las réplicas fueron concatenadas, se filtraron con el programa prinseq-lite-0.20.4. Se cortaron las bases por debajo de Q-score 24 al principio y al final de las lecturas (Secuencias almacenadas en la base de datos SRA del geneBank con el número de acceso BioProject ; PRJNA975118, disponibles a partir del51 24 de Mayo de 2024). Tras el procesamiento, se obtuvieron las secuencias únicas para la eliminación de quimeras y la agrupación en unidades taxonómicas operativas de radio cero (zOTUs) utilizando el algoritmo unoise3 encontrado en Usearch [336–340]. Las secuencias con menos de 400 pb se eliminaron de los datos brutos. Las asignaciones taxonómicas se obtuvieron utilizando el conjunto de secuencias de entrenamiento de la base de datos RDP v16 con un nivel de confianza del 80%. Las comparaciones estadísticas y la visualización de los datos se realizaron mediante R (versión 4.1.0). Para considerar las comparaciones significativas se tuvieron en cuenta los valores p < 0,05, o los valores p ajustados < 0,05. En las comparaciones, para evaluar variables clínicas potencialmente confusas, se utilizó la prueba t de Student, la prueba de rangos con signo de Wilcoxon o la prueba exacta de Fisher, dependiendo del tipo y la distribución de cada variable. Las figuras se trazaron con ggplot2 (v. 2_3.3.5) principalmente. El análisis de la estructura y composición de la comunidad bacteriana se realizó mediante el paquete R phyloseq [349] (v. 1.36.0). El tamaño de la biblioteca de cada muestra se normalizó utilizando la rarefacción para igualar la profundidad de la secuencia y excluir los taxones presentes en menos del 20% de todas las muestras. La diversidad y la riqueza bacteriana se analizaron utilizando Phyloseq con los índices de diversidad alfa (riqueza observada, índice de Shannon e índice de Simpson inverso). Se utilizó la prueba de suma de rangos de Wilcoxon para evaluar las diferencias de diversidad alfa. Para evaluar la diversidad beta, se utilizó phyloseq (v. 1.36.0). Se utilizaron medidas de distancia como la métrica de distancia ponderada y no ponderada de UniFrac para la ordenación sin restricciones de las proporciones de géneros entre los grupos de EP y controles. El análisis multivariado permutado de la varianza PERMANOVA, se ejecutó con el comando ANOSIM (Análisis de similitudes) y Adonis (Permutational Multivariate Analysis of variance using Distance Matrices) parámetro perm = 9999 implementado en vegan (v. 2.5-7) [341]. Estos fueron trazados por el escalamiento multidimensional no métrico (NMDS) utilizando ggplot2 (v. 2_3.3.5) paquetes en el software R.52 Evaluamos la abundancia diferencial entre los grupos de EP y controles mediante la prueba de expresión diferencial basada en un modelo que utiliza la distribución binomial negativa DESeq2 (v. 1.32.0) utilizando el paquete Phyloseq. Este análisis realiza una normalización de las bibliotecas, tiene en cuenta sólo las zOTUs comunes de los dos grupos y obtiene un valor corregido de diferencias significativas [342]. Para la elección de los géneros se toman aquellas zOTUs que se encuentran entre las 100 primeras más abundantes, es decir, se tomaron aquellas zOTUs que están por encima de una abundancia relativa de 0.1. Y realizamos comparaciones en tres niveles taxonómicos (zOTU, género y familia), entre los grupos de EP y controles. Obtención de datos nutricionales y de la enfermedad Con base en los mismos sujetos de estudio y toma de muestras detallada en el capítulo 1 una nutricionista diplomada desarrolló una evaluación nutricional completa adaptada a los fines de la investigación en los 50 sujetos (25 casos y 25 controles) mediante una visita domiciliaria entre junio de 2018 y febrero de 2019. Se obtuvo información sobre diferentes datos clínicos entre los que se incluyen Deposiciones por semana, Escala de Bristol (evaluar de forma descriptiva y gráfica siete tipos de heces, según su forma y consistencia [408]), Escala de Webster, Perímetro de pantorrilla, Escala de gravedad de la enfermedad, la cual es basada en la Escala de Hoehn y Yahr que es utilizada para medir la progresión de los síntomas de la enfermedad y el nivel de discapacidad [409], medicamentos consumidos, otras patologías, entre otras.76 Obtención de componentes de los alimentos La evaluación nutricional incluyó un test de screening de desnutrición con la herramienta de screening de Ferguson [410], la historia nutricional, la evaluación antropométrica, el patrón alimentario y la ingesta nutricional. Se evaluó la ingesta de alimentos con dos recordatorios de 24 horas de los Pasos Múltiples diseñados por el Departamento de Agricultura de los Estados Unidos (USDA). El patrón alimentario de los últimos seis meses se identificó mediante un Cuestionario de Frecuencia Alimentaria cualitativo modificado según la Encuesta Nacional de Situación Nutricional de 2005 y 2010 [411], las Guías Alimentarias para los Estadounidenses y los nutrientes trazadores de riesgo relativos a la composición de la microbiota y la enfermedad de Parkinson. Cada alimento fue codificado y analizado utilizando tanto la Tabla Colombiana de Composición de Alimentos - 2015 [412], como la Lista de Composición de Alimentos del USDA [413], Finlandia [414] y Alemania [415], que comprende 60 nutrientes. Adicionalmente, se evaluó el cuestionario de frecuencia de alimentos según la periodicidad en una escala de 10 puntos avalada por el USDA [416]. Evaluación de datos nutricionales En la evaluación antropométrica se utilizó el peso, la estatura o la estimación de la altura del talón de la rodilla con la ecuación desarrollada para Chumlea [417] . Se evaluó la masa muscular con las circunferencias del brazo y la pantorrilla y la masa grasa con la circunferencia del pliegue cutáneo del tríceps. En el análisis se añadió el IMC estratificado por el punto de corte para adultos [418] y adultos mayores [419]. Estimación de la masa muscular y grasa según los puntos de corte establecidos en la Tercera Encuesta Nacional de Salud y Nutrición (NHANES) [420] y Frisancho [421] para la circunferencia muscular del brazo.77 Adicionalmente, se contrastó la ingesta de nutrientes siguiendo las recomendaciones dietéticas establecidas en la Resolución colombiana 3803 de 2016. Para ello se tomaron las Recomendaciones de Ingesta de Energía y Nutrientes (RIEN), que es la adaptación de las Ingestas Dietéticas de Referencia (DRI). El valor de la ingesta de cada nutriente se comparó con los siguientes valores: Rango de Distribución de Macronutrientes Adecuado (RDA), EAR (Requerimiento Medio Estimado), Cantidades Dietéticas Recomendadas (RDA), AI (Ingesta Adecuada) o UL (Nivel de Ingesta Superior Tolerable) para establecer si la ingesta de cada nutriente era deficit o supera el valor recomendado. Sólo se tomaron para el análisis los nutrientes indicados en las recomendaciones. Análisis estadístico Las comparaciones estadísticas y la visualización de los datos se realizaron mediante R (versión 4.1.0). Para considerar las comparaciones significativas se tuvieron en cuenta los valores p < 0,05, o los valores p ajustados < 0,05. En las comparaciones para evaluar variables clínicas potencialmente confusas, se utilizó la prueba t de Student, la prueba de rangos con signo de Wilcoxon o la prueba exacta de Fisher, dependiendo del tipo y la distribución de cada variable. Las figuras se trazaron con ggplot2 (v. 2_3.3.5). Las variables de nutrientes (metabolitos o elementos alimentarios) de la dieta de los pacientes y los controles se ajustaron por la ingesta energética (dividida por la ingesta energética total en kilocalorías y multiplicada por la media de kilocalorías o dividida por la ingesta energética total en kilocalorías y multiplicada por 1000 ). A partir de los cuestionarios de consumo de 24 H se obtuvo la información correspondiente de cada participante, la cantidad de cada alimento y nutrientes pertenecientes a cada alimento (metabolitos de la dieta). Con el fin de explorar posibles patrones dietarios, se utilizó el Análisis de Componentes Principales (PCA) de la energía ajustada. La correlación entre las variables se calculó utilizando la correlación de rangos de Spearman y la prueba de rangos con signo de Wilcoxon.78 Reconstrucción de la comunidad bacteriana personalizada Para predecir los procesos metabólicos de la comunidad microbiana, integramos la información de abundancia de cada individuo basada en la colección AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) (Versión 1.02). La colección AGORA está compuesta por 818 reconstrucciones metabólicas de especies microbianas comunes del intestino humano. Estos modelos se utilizan para el análisis del flujo metabólico como el FBA (Flux Balance Analysis). Así, tomamos las secuencias filtradas del gen 16S rRNA pertenecientes a las zOTUs obtenidas y las mapeamos a los modelos correspondientes contenidos en una colección refinada de los modelos publicados originalmente en la colección AGORA usando el paquete MicrobiomeAGORA [477,478,479]. Para ello tomamos el 97% como identidad de secuencia mínima y así encontramos el vecino más cercano basado en las secuencias 16S. Análisis individualizado de reconstrucción de la comunidad bacteriana Para predecir la composición funcional del microbioma intestinal a partir de los datos del 16S, utilizamos PICRUSt2 (v 2-2.3.0-6) entre los grupos de EP y de control.108 Como se ha dicho antes, para modelar el comportamiento de la comunidad de la composición del microbioma previamente identificada, utilizamos el paquete de R llamado BacArena [314], aquí las bacterias se representan como individuos situados en rejillas, un lugar donde pueden moverse aleatoriamente y son capaces de tomar e intercambiar metabolitos que se producen/consumen o se encuentran en el entorno. El entorno se compone de 3600 celdas de rejilla donde los modelos de microbios pueden crecer y cerca de 300-500 reconstrucciones de microbios añadidos inicialmente en función de las abundancias relativas. Con un total de 12H pasos de tiempo de crecimiento de cada uno de los modelos microbianos, esto teniendo en cuenta que existe una aproximación del ritmo de alimentación y ayuno durante el día de 12 h. Realizamos el modelado basado en restricciones utilizando el paquete de R Sybil [323] donde la biomasa es el objetivo de la optimización en el FBA [318] y ILOG CPLEX [484] como solucionador de programación lineal. Esta simulación se realizó utilizando 10 réplicas en paralelo (Anexo P). En estos modelos de comunidad intestinal bacteriana específica para cada individuo, obtuvimos la concentración de productos metabólicos finales como los AGCCs al final de la simulación y las diferencias significativas entre el control sano y el modelado personalizado de los pacientes con EP fueron probadas usando el modelo lineal generalizado. Software Las simulaciones y su análisis se realizaron en el entorno R (4.1.0 y 3.6.1) [485]. Se utilizaron los paquetes BacArena [486] y sybil (versión 2.2.0 ) [323]. Además se utilizaron los paquetes: sybilSBML (versión 3.1.2) [487], el solucionador de programación lineal CPLEX (versión 12.7.1) [488], el paquete R cplexAPI (versión 1.4.0) [489]. Para la computación en paralelo, se utilizó el software parallel, con el paquete foreach (versión 1.5.2 ), doParallel (versión 1.0.17) [490–492].Reconstrucción y análisis de redes biológicas Investigación283 páginas +1 anexoapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Medicina - Doctorado en Ciencias BiomédicasFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::616 - Enfermedades600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionadosParkinson Disease/pathologyGastrointestinal ContentsModels, TheoreticalContenido DigestivoEnfermedad de Parkinson/patologíaModelos TeóricosEnfermedad de ParkinsonMicrobioma intestinalModelamiento computacionalModelamiento metabólicoDietaParkinson's diseaseDietGut microbiomeComputational modelingMetabolic modelingModelado computacional de la composición bacteriana intestinal en el contexto de la enfermedad de ParkinsonComputational modeling of intestinal bacterial composition in the context of Parkinson's diseaseTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDBireme1. 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Rep. 2015, 5, 15926, doi:10.1038/srep15926.Universidad JaverianaUniversidad de Kiel AlemaniaEstudiantesInvestigadoresMaestrosMedios de comunicaciónPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84412/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1018441690.2023.pdf1018441690.2023.pdfTesis de Doctorado en Ciencias Biomédicasapplication/pdf18686824https://repositorio.unal.edu.co/bitstream/unal/84412/2/1018441690.2023.pdf81ddb227641c104017d52b938f45781cMD521018441690.2023.Anexos_L_M_N_O.xlsx1018441690.2023.Anexos_L_M_N_O.xlsxAnexos M, N, Oapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheet322779https://repositorio.unal.edu.co/bitstream/unal/84412/3/1018441690.2023.Anexos_L_M_N_O.xlsx1aacf4dab676d50f9867ae3c33ef61f6MD53THUMBNAIL1018441690.2023.pdf.jpg1018441690.2023.pdf.jpgGenerated 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