Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa
ilustraciones, gráficas, tablas
- Autores:
-
Clavijo Buriticá, Diana Carolina
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2018
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80752
- Palabra clave:
- 570 - Biología::572 - Bioquímica
Quorum-Sensing
Reconstrucción de redes metabólicas
Pseudomonas aeruginosa
Pioverdina
Análisis de balance de flujo
Metabolic network reconstruction
Pyoverdine
Flux balance analysis
Genoma
Biología celular
Genome
Cell biology
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa |
| dc.title.translated.por.fl_str_mv |
Reconstrução, modelagem e simulação da rede metabólica e Quorum-Sensing envolvidas na regulação de um fenótipo específico em Pseudomonas aeruginosa |
| dc.title.translated.eng.fl_str_mv |
Reconstruction, modeling and simulation of the metabolic and Quorum-Sensing network involved in the regulation of a specific phenotype in Pseudomonas aeruginosa |
| title |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa |
| spellingShingle |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa 570 - Biología::572 - Bioquímica Quorum-Sensing Reconstrucción de redes metabólicas Pseudomonas aeruginosa Pioverdina Análisis de balance de flujo Metabolic network reconstruction Pyoverdine Flux balance analysis Genoma Biología celular Genome Cell biology |
| title_short |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa |
| title_full |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa |
| title_fullStr |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa |
| title_full_unstemmed |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa |
| title_sort |
Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa |
| dc.creator.fl_str_mv |
Clavijo Buriticá, Diana Carolina |
| dc.contributor.advisor.none.fl_str_mv |
Arévalo Ferro, Catalina González Barrios, Andrés Fernando |
| dc.contributor.author.none.fl_str_mv |
Clavijo Buriticá, Diana Carolina |
| dc.contributor.researchgroup.spa.fl_str_mv |
Comunicación y Comunidades Bacterianas |
| dc.subject.ddc.spa.fl_str_mv |
570 - Biología::572 - Bioquímica |
| topic |
570 - Biología::572 - Bioquímica Quorum-Sensing Reconstrucción de redes metabólicas Pseudomonas aeruginosa Pioverdina Análisis de balance de flujo Metabolic network reconstruction Pyoverdine Flux balance analysis Genoma Biología celular Genome Cell biology |
| dc.subject.proposal.spa.fl_str_mv |
Quorum-Sensing Reconstrucción de redes metabólicas Pseudomonas aeruginosa Pioverdina Análisis de balance de flujo |
| dc.subject.proposal.eng.fl_str_mv |
Metabolic network reconstruction Pyoverdine Flux balance analysis |
| dc.subject.unesco.spa.fl_str_mv |
Genoma Biología celular |
| dc.subject.unesco.eng.fl_str_mv |
Genome Cell biology |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018-10-19 |
| dc.date.accessioned.none.fl_str_mv |
2021-12-02T20:49:07Z |
| dc.date.available.none.fl_str_mv |
2021-12-02T20:49:07Z |
| 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/80752 |
| 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/80752 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.references.spa.fl_str_mv |
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Chincholkar (Eds.), Microbial Siderophores (Vol. 12, pp. 233–242). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-71160- 5_12 Choi, C., Munch, R., Leupold, S., Klein, J., Siegel, I., Thielen, B., ... Jahn, D. (2007). SYSTOMONAS -- an integrated database for systems biology analysis of Pseudomonas. Nucleic Acids Research, 35(Database), D533–D537. http://doi.org/10.1093/nar/gkl823 Chu, B. C., Garcia-Herrero, A., Johanson, T. H., Krewulak, K. D., Lau, C. K., Peacock, R. S., ... Vogel, H. J. (2010). Siderophore uptake in bacteria and the battle for iron with the host; a bird’s eye view. Biometals : An International Journal on the Role of Metal Ions in Biology, Biochemistry, and Medicine, 23(4), 601–11. http://doi.org/10.1007/s10534-010-9361-x Cobessi, D., Celia, H., Folschweiller, N., Schalk, I. J., Abdallah, M. A., & Pattus, F. (2005). The crystal structure of the pyoverdine outer membrane receptor FpvA from Pseudomonas aeruginosa at 3.6 ?? resolution. 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Retrieved from http://www.revistareduca.es/index.php/biologia/article/view/821 Diggle, S. P., Matthijs, S., Wright, V. J., Fletcher, M. P., Chhabra, S. R., Lamont, I. L., ... Williams, P. (2007). The Pseudomonas aeruginosa 4-Quinolone Signal Molecules HHQ and PQS Play Multifunctional Roles in Quorum Sensing and Iron Entrapment. Chemistry and Biology, 14(1), 87–96. http://doi.org/10.1016/j.chembiol.2006.11.014 Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., ... Palsson, B. Ø. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences of the United States of America, 104(6), 1777–82. http://doi.org/10.1073/pnas.0610772104 Elbourne, L. D. H., Tetu, S. G., Hassan, K. A., & Paulsen, I. T. (2017). TransportDB 2.0: a database for exploring membrane transporters in sequenced genomes from all domains of life. 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Derechos reservados al autor, 2021 |
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http://purl.org/coar/access_right/c_abf2 |
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Reconocimiento 4.0 Internacional |
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http://creativecommons.org/licenses/by/4.0/ |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Doctorado en Ciencias - Biología |
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Departamento de Biología |
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Facultad de Ciencias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Reconocimiento 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arévalo Ferro, Catalina80c5a430cc7815abffdfbff36b507731González Barrios, Andrés Fernandoafc37f14623335a945066ef9290b8aaaClavijo Buriticá, Diana Carolinaa8928c158fafd095c271e34cd32a6ed4Comunicación y Comunidades Bacterianas2021-12-02T20:49:07Z2021-12-02T20:49:07Z2018-10-19https://repositorio.unal.edu.co/handle/unal/80752Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasUna meta importante en el periodo posterior al desarrollo de las técnicas de secuenciación de nueva generación es la de relacionar las secuencias anotadas de los genomas con las funciones fisiológicas de una célula. Es por esto que la Biología de Sistemas ha venido trabajando en el diseño de nuevas metodologías computacionales para la reconstrucción de redes metabólicas a escala genómica y para el modelamiento y la simulación dinámica de estos sistemas biológicos en busca de estudiar la regulación de los mecanismos biológicos para la expresión de fenotipos. Debido a la facilidad relativa para la obtención de datos, el tamaño de sus genomas, los costos asociados y el interés clínico entre otras razones, los microorganismos son el grupo en el cual mayor cantidad de redes metabólicas se han reconstruido. Entre los mecanismos que controlan la expresión genética, el Quorum-Sensing es relevante no solo desde la perspectiva de las ciencias básicas, sino que se considera un eslabón importante para lograr avances en el área biotecnológica. El fenómeno conocido como Quorum-Sensing (QS) se basa en la comunicación celular mediada por moléculas de señalización y se encarga de sincronizar la expresión de fenotipos en una comunidad bacteriana, por ejemplo, para controlar su metabolismo y sus funciones como comunidad. Tomando como modelo la red metabólica regulada por QS en Pseudomonas aeruginosa (PAO1) para la producción de un factor de virulencia, de la familia de los sideróforos, conocido como pioverdina (PVD), este trabajo buscó generar la reconstrucción, modelamiento y simulación dinámica de esta red. Para esto se empleó el análisis dinámico de balance de flujo (DFBA) en el modelaje de la red metabólica asociada al fenómeno de QS como una estrategia importante para dar solución al interrogante que se plantea en este trabajo: evidenciar en el modelo la influencia del QS de PAO1 en la síntesis de PVD. Para dar cumplimiento a los objetivos que aborda este trabajo, la metodología propuesta comprende tres grandes etapas: (i) Reconstrucción, modelamiento y validación de la red génica de QS que regula la síntesis de PVD en PAO1. (ii) Construcción, curación y modelamiento bajo la aproximación de FBA de la red metabólica de Pseudomonas aeruginosa. Y (iii) unión, modelamiento bajo la aproximación de DFBA y validación experimental in vitro de la red génica de Quorum-Sensing acoplada con la red metabólica de PAO1 para la síntesis de PVD. La red génica de QS que regula la síntesis de PVD en PAO1, se construyó sobre el estándar SBML, consta de 114 especies químicas y biológicas y 103 reacciones. La red de QS fue modelada como un sistema determinista siguiendo los parámetros de la ley de acción de masas. Los resultados mostraron que a medida que aumenta el crecimiento poblacional, aumenta la producción de moléculas señal de QS en el espacio extracelular emulando así el comportamiento natural de un cultivo bacteriano de PAO1. La reconstrucción de la red metabólica se realizó con base en el modelo iMO1056, la anotación del genoma PAO1 y la vía metabólica para la biosíntesis PVD. El modelo metabólico involucra las reacciones de biosíntesis y de transporte e intercambio de PVD y de las moléculas señal de QS. Se realizó la curación de la red y posteriormente se modeló bajo la aproximación de DFBA, empleando como función objetivo la maximización de biomasa. Se seleccionaron nueve reacciones compartidas por la red de QS y la red metabólica para la fusión de ambas redes. Los flujos de estas reacciones de la red de QS, fueron fijados en el sistema metabólico como restricciones del problema de optimización. Utilizando el DFBA, se realizaron las simulaciones del sistema para obtener (i) los perfiles de flujo para cada reacción, (ii) el perfil de crecimiento, (iii) el perfil de biomasa y (iv) los perfiles de concentración de metabolitos de interés tales como moléculas de señalización de QS, glucosa y PVD. La red metabólica propuesta consta de 1124 reacciones y 881 metabolitos (modelo CCBM1737). El modelamiento dinámico de la red metabólica acoplada a la red de QS de PAO1, permitió evidenciar que el fenómeno de QS ejerce una influencia directa sobre la expresión de diferentes fenotipos metabólicos de acuerdo con el cambio de la intensidad de la señal de QS. Este trabajo es el primer reporte de un modelo in silico de la red génica que comprende todos los sistemas de Quorum-Sensing acoplada con la red metabólica de Pseudomonas aeruginosa. (Texto tomado de la fuente)An important goal in the period following the development of new generation sequencing techniques is to relate the annotated sequences of genomes to the physiological functions of a cell. That is why Systems Biology has been working on the design of new computational methodologies for the reconstruction of metabolic networks at genomic scale and for the modeling and dynamic simulation of these biological systems in order to study the regulation of biological mechanisms for the expression of phenotypes. Due to the relative ease of data collection, genome size, associated costs and clinical interest, among other reasons, microorganisms are the group in which most metabolic networks have been rebuilt. Among the mechanisms that control gene expression, the Quorum-Sensing is relevant not only from the perspective of the basic sciences but is also considered an important link to achieve advances in the biotechnological area. The phenomenon known as Quorum-Sensing (QS) is based on cell communication mediated by signaling molecules and is responsible for synchronizing the expression of phenotypes in a bacterial community, for example, to control its metabolism and functions as a community. Taking as a model the metabolic network regulated by QS in Pseudomonas aeruginosa (PAO1) for the production of a virulence factor, of the siderophore family, known as pyoverdine (or this purpose, the dynamic flow balance analysis (DFBA) was used in the modeling of the metabolic network associated with the QS phenomenon as an important strategy to provide a solution to the question posed in this paper: to highlight in the model the influence of the QS of PAO1 on the synthesis of PVD. In order to meet the objectives of this work, the proposed methodology comprises three main stages: (i) Reconstruction, modeling, and validation of the QS gene network that regulates the synthesis of ODP in ODP1. (ii) Construction, healing, and modeling under the ABF approach of the metabolic network of Pseudomonas aeruginosa. And (iii) binding, modeling under the DFBA approach and in vitro experimental validation of the Quorum-Sensing gene network coupled with the PAO1 metabolic network for PVD synthesis. The QS gene network that regulates the synthesis of PVD in PAO1 was built on the SBML standard, consisting of 114 chemical and biological species and 103 reactions. The QS network was modeled as a deterministic system following the parameters of the law of mass action. The results showed that as population growth increases, the production of QS signal molecules in the extracellular space increases, emulating the natural behavior of a bacterial culture of PAO1. The reconstruction of the metabolic network was carried out based on the iMO1056 model, the annotation of the PAO1 genome and the metabolic pathway for PVD biosynthesis. The metabolic model involves the reactions of biosynthesis and transport and exchange of PVD and QS signal molecules. The network was cured and then modeled under the DFBA approach, using biomass maximization as an objective function. Nine reactions shared by the QS network and the metabolic network were selected to merge the two networks. The flows of these reactions from the QS network were set in the metabolic system as restrictions of the optimization problem. Using DFBA, system simulations were performed to obtain (i) the flow profiles for each reaction, (ii) the growth profile, (iii) the biomass profile and (iv) the concentration profiles of metabolites of interest such as QS, glucose, and PVD signaling molecules. The proposed metabolic network consists of 1124 reactions and 881 metabolites (model CCBM1737). The dynamic modeling of the metabolic network coupled to the QS network of PAO1, allowed to show that the QS phenomenon has a direct influence on the expression of different metabolic phenotypes according to the change in the intensity of the QS signal. This paper is the first report of an in silico model of the gene network comprising all Quorum-Sensing systems coupled with the metabolic network of Pseudomonas aeruginosa.Convocatoria 727 Doctorados Nacionales 2015DoctoradoDoctor en Ciencias - BiologíaQuorum-sensing y biofilms bacterianos131 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Doctorado en Ciencias - BiologíaDepartamento de BiologíaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá570 - Biología::572 - BioquímicaQuorum-SensingReconstrucción de redes metabólicasPseudomonas aeruginosaPioverdinaAnálisis de balance de flujoMetabolic network reconstructionPyoverdineFlux balance analysisGenomaBiología celularGenomeCell biologyReconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosaReconstrução, modelagem e simulação da rede metabólica e Quorum-Sensing envolvidas na regulação de um fenótipo específico em Pseudomonas aeruginosaReconstruction, modeling and simulation of the metabolic and Quorum-Sensing network involved in the regulation of a specific phenotype in Pseudomonas aeruginosaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDArevalo-Ferro, C., Hentzer, M., Reil, G., G??rg, A., Kjelleberg, S., Givskov, M., ... 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IET Systems Biology, 5(6), 336–6. http://doi.org/10.1049/iet-syb.2011.0015Reconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing, implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosaMinisterio de Ciencia y TecnologóaInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/80752/1/license.txt8153f7789df02f0a4c9e079953658ab2MD51ORIGINAL52886049_2018.pdf52886049_2018.pdfTesis de Doctorado en Ciencias - Biologíaapplication/pdf7458158https://repositorio.unal.edu.co/bitstream/unal/80752/2/52886049_2018.pdf8e3feaa9c255a697a54f0076b421e691MD52THUMBNAIL52886049_2018.pdf.jpg52886049_2018.pdf.jpgGenerated Thumbnailimage/jpeg6015https://repositorio.unal.edu.co/bitstream/unal/80752/3/52886049_2018.pdf.jpgc1c469c4cd023b081313537116a11bb4MD53unal/80752oai:repositorio.unal.edu.co:unal/807522024-08-01 23:10:43.187Repositorio Institucional Universidad Nacional de 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