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dc.rights.licenseReconocimiento 4.0 Internacional
dc.contributor.advisorArévalo Ferro, Catalina
dc.contributor.advisorGonzález Barrios, Andrés Fernando
dc.contributor.authorClavijo Buriticá, Diana Carolina
dc.date.accessioned2021-12-02T20:49:07Z
dc.date.available2021-12-02T20:49:07Z
dc.date.issued2018-10-19
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80752
dc.descriptionilustraciones, gráficas, tablas
dc.description.abstractUna 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)
dc.description.abstractAn 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.
dc.description.sponsorshipConvocatoria 727 Doctorados Nacionales 2015
dc.format.extent131 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rightsDerechos reservados al autor, 2021
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.ddc570 - Biología::572 - Bioquímica
dc.titleReconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa
dc.typeTrabajo de grado - Doctorado
dc.type.driverinfo:eu-repo/semantics/doctoralThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ciencias - Doctorado en Ciencias - Biología
dc.contributor.researchgroupComunicación y Comunidades Bacterianas
dc.description.degreelevelDoctorado
dc.description.degreenameDoctor en Ciencias - Biología
dc.description.researchareaQuorum-sensing y biofilms bacterianos
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentDepartamento de Biología
dc.publisher.facultyFacultad de Ciencias
dc.publisher.placeBogotá, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.relation.referencesArevalo-Ferro, C., Hentzer, M., Reil, G., G??rg, A., Kjelleberg, S., Givskov, M., ... Eberl, L. (2003). Identification of quorum-sensing regulated proteins in the opportunistic pathogen Pseudomonas aeruginosa by proteomics. Environmental Microbiology, 5(12), 1350–1369. http://doi.org/10.1046/j.1462-2920.2003.00532.x
dc.relation.referencesBabaei, P., Ghasemi-Kahrizsangi, T., & Marashi, S. A. (2014). Modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis: How good are genome-scale metabolic networks at predicting the differences? The Scientific World Journal, 2014. http://doi.org/10.1155/2014/416289
dc.relation.referencesBabaei, P., Marashi, S.-A., & Asad, S. (2015). Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501. Molecular bioSystems, 11(11), 3022–32. http://doi.org/10.1039/c5mb00086f
dc.relation.referencesBartell, J. A., Blazier, A. S., Yen, P., Thøgersen, J. C., Jelsbak, L., Goldberg, J. B., & Papin, J. A. (2017). Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nature Communications, 8, 14631. http://doi.org/10.1038/ncomms14631
dc.relation.referencesBeare, P. a, For, R. J., Martin, L. W., & Lamont, I. L. (2003). Siderophore-mediated cell signalling in Pseudomonas aeruginosa: divergent pathways regulate virulence factor production and siderophore receptor synthesis. Molecular Microbiology, 47(1), 195–207. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12492864
dc.relation.referencesBerthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., ... Wiswedel, B. (2008). KNIME: The Konstanz Information Miner (pp. 319–326). Springer, Berlin, Heidelberg. http://doi.org/10.1007/978-3-540-78246-9_38
dc.relation.referencesBiggs, M. B., & Papin, J. A. (2017). Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA. PLOS Computational Biology, 13(3), e1005413. http://doi.org/10.1371/journal.pcbi.1005413
dc.relation.referencesBlazier, A. S., & Papin, J. A. (2012). Integration of expression data in genome-scale metabolic network reconstructions. Frontiers in Physiology, 3, 299. http://doi.org/10.3389/fphys.2012.00299
dc.relation.referencesBriskot, G., Taraz, K., & Budzikiewicz, H. (1989). Bacterial Constituents, XXXVII. Pyoverdin-Type Siderophores fromPseudomonas aeruginosa. Liebigs Annalen Der Chemie, 1989(4), 375– 384. http://doi.org/10.1002/jlac.198919890164
dc.relation.referencesBultreys, A., Gheysen, I., Maraite, H., & De Hoffmann, E. (2001). Characterization of Fluorescent and Nonfluorescent Peptide Siderophores Produced by Pseudomonas syringae Strains and Their Potential Use in Strain Identification. Applied and Environmental Microbiology, 67(4), 1718–1727. http://doi.org/10.1128/AEM.67.4.1718-1727.2001
dc.relation.referencesCárdenas Barbosa, A. J. (2012). Búsqueda de relaciones entre la comunicación celular bacteriana el potencial de virulencia y la estructura de la comunidad bacteriana en la enfermedad de la plaga blanca tipo II. Universidad Nacional de Colombia. Retrieved from http://www.bdigital.unal.edu.co/12161/
dc.relation.referencesCaspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., ... Karp, P. D. (2014). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Research, 42(D1), 473–479. http://doi.org/10.1093/nar/gkt1103
dc.relation.referencesChaudhary, N., Tøndel, K., Bhatnagar, R., dos Santos, V. A. P. M., & Puchałka, J. (2016). Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling. Molecular bioSystems, 12(3), 994–1005. http://doi.org/10.1039/c5mb00457h
dc.relation.referencesChen, J., Gomez, J. A., Höffner, K., Phalak, P., Barton, P. I., & Henson, M. A. (2016). Spatiotemporal modeling of microbial metabolism. BMC Systems Biology, 10(1), 21. http://doi.org/10.1186/s12918-016-0259-2
dc.relation.referencesChincholkar, S. B., Chaudhari, B. L., & Rane, M. R. (2007). Microbial Siderophore: A State of Art. In A. Varma & S. B. 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
dc.relation.referencesChoi, 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
dc.relation.referencesChu, 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
dc.relation.referencesCobessi, 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. Journal of Molecular Biology, 347(1), 121–134. http://doi.org/10.1016/j.jmb.2005.01.021
dc.relation.referencesConstantinescu, O., Sahnazarov, N., & Muţiu, A. (1994). Chromosomal changes in rat cells transformed in vitro by herpes simplex virus (HSV). Virologie, 29(1), 71–2. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8288518
dc.relation.referencesDas, A., Prasad, R., Srivastava, A., Giang, P. H., Bhatnagar, K., & Varma, A. (2007). Fungal Siderophores: Structure, Functions and Regulation. In Microbial Siderophores (Vol. 12, pp. 1–42). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540- 71160-5_1
dc.relation.referencesDemšar, J., Curk, T., Erjavec, A., Hočevar, T., Milutinovič, M., Možina, M., ... Zupan, B. (2013). Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research, 14, 2349– 2353. Retrieved from http://jmlr.org/papers/volume14/demsar13a/demsar13a.pdf
dc.relation.referencesDíaz, D. M., & Sen, a S. D. La. (2011). Procesos biológicos regulados por quorum sensing. REDUCA (Biología), 3(5), 56–74. Retrieved from http://www.revistareduca.es/index.php/biologia/article/view/821
dc.relation.referencesDiggle, 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
dc.relation.referencesDuarte, 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
dc.relation.referencesElbourne, 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. Nucleic Acids Research, 45(D1), D320–D324. http://doi.org/10.1093/nar/gkw1068
dc.relation.referencesFagerlind, M. G., Nilsson, P., Harl??n, M., Karlsson, S., Rice, S. A., & Kjelleberg, S. (2005). Modeling the effect of acylated homoserine lactone antagonists in Pseudomonas aeruginosa. BioSystems, 80(2), 201–213. http://doi.org/10.1016/j.biosystems.2004.11.008
dc.relation.referencesFagerlind, M. G., Rice, S. A., Nilsson, P., Harl??n, M., James, S., Charlton, T., & Kjelleberg, S. (2003). The role of regulators in the expression of quorum-sensing signals in Pseudomonas aeruginosa. Journal of Molecular Microbiology and Biotechnology, 6(2), 88–100. http://doi.org/10.1159/000076739
dc.relation.referencesFallahzadeh, V., Ahmadzadeh, M., & Sharifi, R. (2010). Growth and pyoverdine production kinetics of Pseudomonas aeruginosa 7NSK2 in an experimental fermentor. J Agric Tech, 6(1), 107–115. Retrieved from http://ijat-aatsea.com/pdf/Jan_v6_n1_10/12-65- IJAT2009_45F.pdf
dc.relation.referencesFeist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L., & Palsson, B. Ø. (2008). Reconstruction of biochemical networks in microorganisms. Nature Reviews Microbiology, 7(2), 129–143. http://doi.org/10.1038/nrmicro1949
dc.relation.referencesFernandez, M. A. (2012). Creación De Un Módulo Sintético De Comunicación Bacteriana Mediante Redes Artificiales De Quorum Sensing. Nacional de Colombia.
dc.relation.referencesFörster, J., Famili, I., Fu, P., Palsson, B. Ø., & Nielsen, J. (2003). Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Research, 13(2), 244–53. http://doi.org/10.1101/gr.234503
dc.relation.referencesFunahashi, A., Matsuoka, Y., Jouraku, A., Kitano, H., & Kikuchi, N. (2006). Celldesigner: A Modeling Tool for Biochemical Networks. In Proceedings of the 2006 Winter Simulation Conference (pp. 1707–1712). IEEE. http://doi.org/10.1109/WSC.2006.322946
dc.relation.referencesFuqua, C., & Greenberg, E. P. (2002). Listening in on bacteria: acyl-homoserine lactone signalling. Nature Reviews Molecular Cell Biology, 3(9), 685–695. http://doi.org/10.1038/nrm907
dc.relation.referencesFuqua, C., Parsek, M. R., & Greenberg, E. P. (2001). Regulation of Gene Expression by Cell-to- Cell Communication: Acyl-Homoserine Lactone Quorum Sensing. Annual Review of Genetics, 35(1), 439–468. http://doi.org/10.1146/annurev.genet.35.102401.090913
dc.relation.referencesGanter, M., Bernard, T., Moretti, S., Stelling, J., & Pagni, M. (2013). MetaNetX.org: a website and repository for accessing, analysing and manipulating metabolic networks. Bioinformatics, 29(6), 815–816. http://doi.org/10.1093/bioinformatics/btt036
dc.relation.referencesGianchandani, E. P., Chavali, A. K., & Papin, J. A. (2010). The application of flux balance analysis in systems biology. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2(3), 372–382. http://doi.org/10.1002/wsbm.60
dc.relation.referencesGiuseppin, M. L. F., & van Riel, N. A. W. (2000). Metabolic Modeling of Saccharomyces cerevisiae Using the Optimal Control of Homeostasis: A Cybernetic Model Definition. Metabolic Engineering, 2(1), 14–33. http://doi.org/10.1006/mben.1999.0134
dc.relation.referencesGivskov, M., Rasmussen, T . B., Ren, D., & Balaban, N. (2008). Bacterial Cell-to-cell Communication (Quorum Sensing). In Control of Biofilm Infections by Signal Manipulation (pp. 13–38). http://doi.org/10.1007/978-3-540-73853-4
dc.relation.referencesGonzalez Barrios, A. F., & Achenie, L. E. K. (2010). Escherichia coli autoinducer-2 uptake network does not display hysteretic behavior but AI-2 synthesis rate controls transient bifurcation. Biosystems, 99(1), 17–26. http://doi.org/10.1016/j.biosystems.2009.08.003
dc.relation.referencesGonzalez Barrios, A. F., Covo, V., Medina, L. M., Vives-Florez, M., & Achenie, L. (2009). Quorum quenching analysis in Pseudomonas aeruginosa and Escherichia coli: network topology and inhibition mechanism effect on the optimized inhibitor dose. Bioprocess and Biosystems Engineering, 32(4), 545–556. http://doi.org/10.1007/s00449-008-0276-7
dc.relation.referencesGonzález Barrios, A. F., & Florez, D. (2010). (601a) Is It Indole Language or Food? Phase Plane Analysis of Indole as Quorum Sensing Signal in E. Coli | AIChE Academy. In 2010 AIChE Annual Meeting (pp. 321–321). Salt Lake City, Ut: American Institute Of Chemical Engineering Aiche. Retrieved from https://www.aiche.org/conferences/aiche-annual- meeting/2010/proceeding/paper/601a-it-indole-language-or-food-phase-plane-analysis- indole-quorum-sensing-signal-e-coli
dc.relation.referencesGray, K. M. (1997). Intercellular communication and group behavior in bacteria. Trends in Microbiology, 5(5), 184–8. http://doi.org/10.1016/S0966-842X(97)01002-0
dc.relation.referencesGreenwald, J., Hoegy, F., Nader, M., Journet, L., Mislin, G. L. a, Graumann, P. L., & Schalk, I. J. (2007). Real time fluorescent resonance energy transfer visualization of ferric pyoverdine uptake in Pseudomonas aeruginosa. A role for ferrous iron. The Journal of Biological Chemistry, 282(5), 2987–95. http://doi.org/10.1074/jbc.M609238200
dc.relation.referencesGrivell, L. (2002). Mining the bibliome: searching for a needle in a haystack?: New computing tools are needed to effectively scan the growing amount of scientific literature for useful information. EMBO Reports, 3(3), 200–203. http://doi.org/10.1093/embo-reports/kvf059
dc.relation.referencesHaggart, C. R., Bartell, J. A., Saucerman, J. J., & Papin, J. A. (2011). Whole-genome metabolic network reconstruction and constraint-based modeling. Methods in Enzymology, 500, 411– 33. http://doi.org/10.1016/B978-0-12-385118-5.00021-9
dc.relation.referencesHannauer, M., Sch??fer, M., Hoegy, F., Gizzi, P., Wehrung, P., Mislin, G. L. A., ... Schalk, I. J. (2012). Biosynthesis of the pyoverdine siderophore of Pseudomonas aeruginosa involves precursors with a myristic or a myristoleic acid chain. FEBS Letters, 586(1), 96–101. http://doi.org/10.1016/j.febslet.2011.12.004
dc.relation.referencesHeeb, S., Fletcher, M. P., Chhabra, S. R., Diggle, S. P., Williams, P., & Cámara, M. (2011). Quinolones: From antibiotics to autoinducers. FEMS Microbiology Reviews, 35(2), 247–274. http://doi.org/10.1111/j.1574-6976.2010.00247.x
dc.relation.referencesHentzer, M., Wu, H., Andersen, J. B., Riedel, K., Rasmussen, T. B., & Bagge, N. (2003). Attenuation of Pseudomonas aeruginosa virulence by quorum-sensing inhibitors. Embo J., 22(15), 3803.
dc.relation.referencesHerrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., ... Kell, D. B. (2008). A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology, 26(10), 1155–60. http://doi.org/10.1038/nbt1492
dc.relation.referencesHu, D., & Yuan, J.-M. (2006). Time-Dependent Sensitivity Analysis of Biological Networks: Coupled MAPK and PI3K Signal Transduction Pathways †. The Journal of Physical Chemistry A, 110(16), 5361–5370. http://doi.org/10.1021/jp0561975
dc.relation.referencesIhekwaba, A. E., Broomhead, D. S., Grimley, R. L., Benson, N., & Kell, D. B. (2004). Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha. Systems Biology, 1(1), 93–103. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/17052119
dc.relation.referencesImperi, F., Tiburzi, F., & Visca, P. (2009). Molecular basis of pyoverdine siderophore recycling in Pseudomonas aeruginosa. Proceedings of the National Academy of Sciences of the United States of America, 106(48), 20440–20445. http://doi.org/10.1073/pnas.0908760106
dc.relation.referencesImperi, F., & Visca, P. (2013). Subcellular localization of the pyoverdine biogenesis machinery of Pseudomonas aeruginosa: A membrane-associated “siderosome.” FEBS Letters, 587(21), 3387–3391. http://doi.org/10.1016/j.febslet.2013.08.039
dc.relation.referencesJimenez, P. N., Koch, G., Thompson, J. A., Xavier, K. B., Cool, R. H., & Quax, W. J. (2012). The multiple signaling systems regulating virulence in Pseudomonas aeruginosa. Microbiology and Molecular Biology Reviews : MMBR, 76(1), 46–65. http://doi.org/10.1128/MMBR.05007-11
dc.relation.referencesJohnson, T. B. (2009). Developing Stochastic and Deterministic Models of Quorum Sensing (ArchivedREUpapers). Boston. Retrieved from http://dept.math.lsa.umich.edu/undergrad/REU/ArchivedREUpapers/2009 Papers/Tyler Johnson 2009.pdf
dc.relation.referencesJuhas, M. (2004). Global regulation of quorum sensing and virulence by VqsR in Pseudomonas aeruginosa. Microbiology, 150(4), 831–841. http://doi.org/10.1099/mic.0.26906-0
dc.relation.referencesKanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017). KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Research, 45(D1), D353–D361. http://doi.org/10.1093/nar/gkw1092
dc.relation.referencesKanehisa, M., & Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27–30. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10592173
dc.relation.referencesKanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K. F., Itoh, M., Kawashima, S., ... Hirakawa, M. (2006). From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Research, 34(Database issue), D354-7. http://doi.org/10.1093/nar/gkj102
dc.relation.referencesKhatri, P., Sirota, M., & Butte, A. J. (2012). Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges. PLoS Computational Biology, 8(2), e1002375. http://doi.org/10.1371/journal.pcbi.1002375
dc.relation.referencesKievit, T. R. D. E., Gillis, R., Marx, S., & Brown, C. (2001). Quorum-Sensing Genes in Pseudomonas aeruginosa Biofilms : Their Role and Expression Patterns. Applied and Environmental Microbiology, 67(4), 1865–1873. http://doi.org/10.1128/AEM.67.4.1865
dc.relation.referencesKim, D., Chung, S., Lee, S., & Choi, J. (2012). Relation of microbial biomass to counting units for Pseudomonas aeruginosa. African Journal of Microbiology Research, 6(21), 4620–4622. http://doi.org/10.5897/AJMR10.902
dc.relation.referencesKim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., ... Bryant, S. H. (2016). PubChem Substance and Compound databases. Nucleic Acids Research, 44(D1), D1202– D1213. http://doi.org/10.1093/nar/gkv951
dc.relation.referencesKing, Z. A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J. A., ... Lewis, N. E. (2016). BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Research, 44(D1), D515–D522. http://doi.org/10.1093/nar/gkv1049
dc.relation.referencesKitano, H. (2002). Computational systems biology. Nature, 420(6912), 206–10. http://doi.org/10.1038/nature01254
dc.relation.referencesKumar, V. S., Dasika, M. S., & Maranas, C. D. (2007). Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics, 8, 212. http://doi.org/10.1186/1471-2105-8- 212
dc.relation.referencesLamont, I. L., & Martin, L. W. (2003). Identification and characterization of novel pyoverdine synthesis genes in Pseudomonas aeruginosa. Microbiology, 149(4), 833–842. http://doi.org/10.1099/mic.0.26085-0
dc.relation.referencesLazdunski, A. M., Ventre, I., & Bleves, S. (2007). Cell–Cell Communication: Quorum Sensing and Regulatory Circuits in Pseudomonas aeruginosa. In J.-L. Ramos & A. Filloux (Eds.), Pseudomonas (pp. 279–310). Dordrecht: Springer Netherlands. http://doi.org/10.1007/978- 1-4020-6097-7_10
dc.relation.referencesLee, K.-M., Yoon, M. Y., Park, Y., Lee, J.-H., & Yoon, S. S. (2011). Anaerobiosis-induced loss of cytotoxicity is due to inactivation of quorum sensing in Pseudomonas aeruginosa. Infection and Immunity, 79(7), 2792–800. http://doi.org/10.1128/IAI.01361-10
dc.relation.referencesLeoni, L., Orsi, N., Lorenzo, V. De, & Visca, P. (2000). Functional Analysis of PvdS , an Iron Starvation Sigma Factor of Pseudomonas aeruginosa Functional Analysis of PvdS , an Iron Starvation Sigma Factor of Pseudomonas aeruginosa Downloaded from http://jb.asm.org/ on September 17 , 2013 by CALIFORNIA INSTITU. Journal of Bacteriology, 182(6), 1481– 1491. http://doi.org/10.1128/JB.182.6.1481-1491.2000.Updated
dc.relation.referencesLi, L. L., Malone, J. E., & Iglewski, B. H. (2007). Regulation of the Pseudomonas aeruginosa quorum-sensing regulator VqsR. Journal of Bacteriology, 189(12), 4367–4374. http://doi.org/10.1128/JB.00007-07
dc.relation.referencesLiu, Z., Lanford, R., Mueller, S., Gerhard, G. S., Luscieti, S., Sanchez, M., & Devireddy, L. (2012). Siderophore-mediated iron trafficking in humans is regulated by iron. Journal of Molecular Medicine (Berlin, Germany), 90(10), 1209–21. http://doi.org/10.1007/s00109-012-0899-7
dc.relation.referencesMachne, R., Finney, A., Muller, S., Lu, J., Widder, S., & Flamm, C. (2006). The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks. Bioinformatics, 22(11), 1406–1407. http://doi.org/10.1093/bioinformatics/btl086
dc.relation.referencesMahadevan, R., Edwards, J. S., & Doyle, F. (2002). Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophysical Journal, 83(3), 1331–1340. http://doi.org/10.1016/S0006-3495(02)73903-9
dc.relation.referencesMaksimova, N., Blazhevich, O., & Fomichev, I. (1993). The role of pyrimidines in the biosynthesis of the fluorescing pigment pyoverdin Pm in Pseudomonas putida M. Molekuliarnaia Genetika, Mikrobiologiia I Virusologiia, (5), 22–6. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8289842
dc.relation.referencesMaranas, C. D., & Zomorrodi, A. R. (2016). Optimization Methods in Metabolic Networks. Retrieved from https://books.google.com/books?hl=en&lr=&id=FYebCgAAQBAJ&oi=fnd&pg=PP13&dq=O ptimization+Methods+in+Metabolic+Networks&ots=KBJNd2TWQm&sig=WN3mly_BPQ9Es nrrMhqtnHxqXHc
dc.relation.referencesMcMorran, B. J., Shantha Kumara, H. M. C., Sullivan, K., & Lamont, I. L. (2001). Involvement of a transformylase enzyme in siderophore synthesis in Pseudomonas aeruginosa. Microbiology, 147(6), 1517–1524.
dc.relation.referencesMeyer, J.-M. (2007). Siderotyping and Bacterial Taxonomy: A Siderophore Bank for a Rapid Identification at the Species Level of Fluorescent and Non-Fluorescent Pseudomonas. In Microbial Siderophores (Vol. 12, pp. 43–65). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-71160-5_2
dc.relation.referencesMEYER, J. M., & ABDALLAH, M. A. (1978). The Fluorescent Pigment of Pseudomonas fluorescens: Biosynthesis, Purification and Physicochemical Properties. Journal of General Microbiology, 107(2), 319–328. http://doi.org/10.1099/00221287-107-2-319
dc.relation.referencesMiller, M. B., & Bassler, B. L. (2001). Quorum Sensing in Bacteria. Annual Review of Microbiology, 55(1), 165–199. http://doi.org/10.1146/annurev.micro.55.1.165
dc.relation.referencesMonk, J., Nogales, J., & Palsson, B. O. (2014). Optimizing genome-scale network reconstructions. Nature Biotechnology, 32(5), 447–452. http://doi.org/10.1038/nbt.2870
dc.relation.referencesMoon, C. D., Zhang, X.-X., Matthijs, S., Schäfer, M., Budzikiewicz, H., & Rainey, P. B. (2008). Genomic, genetic and structural analysis of pyoverdine-mediated iron acquisition in the plant growth-promoting bacterium Pseudomonas fluorescens SBW25. BMC Microbiology, 8(1), 7. http://doi.org/10.1186/1471-2180-8-7
dc.relation.referencesMossialos, D., Ochsner, U., Baysse, C., Chablain, P., Pirnay, J.-P., Koedam, N., ... Cornelis, P. (2002). Identification of new, conserved, non-ribosomal peptide synthetases from fluorescent pseudomonads involved in the biosynthesis of the siderophore pyoverdine. Molecular Microbiology, 45(6), 1673–85. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12354233
dc.relation.referencesMurabito, E., Simeonidis, E., Smallbone, K., & Swinton, J. (2009). Capturing the essence of a metabolic network: A flux balance analysis approach. Journal of Theoretical Biology, 260(3), 445–452. http://doi.org/10.1016/j.jtbi.2009.06.013
dc.relation.referencesNiazy, A., & Hughes, L. E. (2015). Accumulation of Pyrimidine Intermediate Orotate Decreases Virulence Factor Production in Pseudomonas aeruginosa. Current Microbiology, 71(2), 229– 234. http://doi.org/10.1007/s00284-015-0826-6
dc.relation.referencesNoble, D. (2002). The rise of computational biology. Nature Reviews. Molecular Cell Biology, 3(6), 459–63. http://doi.org/10.1038/nrm810
dc.relation.referencesNogales, J., Palsson, B., & Thiele, I. (2008). A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Systems Biology, 2(1), 79. http://doi.org/10.1186/1752-0509-2-79
dc.relation.referencesO’Toole, G. A., & Monds, R. D. (2008). Metabolites as Intercellular Signals for Regulation of Community-Level Traits. In S. C. Winans & B. L. Bassler (Eds.), Chemical Communication among Bacteria (1st ed., pp. 105–129). American Society of Microbiology. http://doi.org/10.1128/9781555815578.ch8
dc.relation.referencesOberhardt, M. A., Goldberg, J. B., Hogardt, M., & Papin, J. A. (2010). Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung infection. Journal of Bacteriology, 192(20), 5534–48. http://doi.org/10.1128/JB.00900-10
dc.relation.referencesOberhardt, M. A., Puchalka, J., Fryer, K. E., Martins dos Santos, V. A. P., & Papin, J. A. (2008). Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1. Journal of Bacteriology, 190(8), 2790–2803. http://doi.org/10.1128/JB.01583-07
dc.relation.referencesOberhardt, M. A., Puchałka, J., Martins dos Santos, V. A. P., & Papin, J. A. (2011). Reconciliation of Genome-Scale Metabolic Reconstructions for Comparative Systems Analysis. PLoS Computational Biology, 7(3), e1001116. http://doi.org/10.1371/journal.pcbi.1001116
dc.relation.referencesOberhardt, M. a, Palsson, B. Ø., & Papin, J. a. (2009). Applications of genome-scale metabolic reconstructions. Molecular Systems Biology, 5(320), 320. http://doi.org/10.1038/msb.2009.77
dc.relation.referencesOrth, J. D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis? Nature Publishing Group, 28(3), 245–248. http://doi.org/10.1038/nbt.1614
dc.relation.referencesPalsson, B. (2006). Systems biology : properties of reconstructed networks. Cambridge University Press. Retrieved from http://www.cambridge.org/gb/academic/subjects/life- sciences/genomics-bioinformatics-and-systems-biology/systems-biology-properties- reconstructed-networks?format=HB&isbn=9780521859035#DvsDe1JZPIey6XwF.97
dc.relation.referencesPrzulj, N. (2007). Biological network comparison using graphlet degree distribution. Bioinformatics, 23(2), e177–e183. http://doi.org/10.1093/bioinformatics/btl301
dc.relation.referencesPuchalka, J., Oberhardt, M. A., Godinho, M., Bielecka, A., Regenhardt, D., Timmis, K. N., ... Martins Dos Santos, V. A. P. (2008). Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology. PLoS Computational Biology, 4(10). http://doi.org/10.1371/journal.pcbi.1000210
dc.relation.referencesRaman, K., & Chandra, N. (2009). Flux balance analysis of biological systems: Applications and challenges. Briefings in Bioinformatics, 10(4), 435–449. http://doi.org/10.1093/bib/bbp011
dc.relation.referencesRamkrishna, D., & Song, H.-S. (2012). Dynamic models of metabolism: Review of the cybernetic approach. AIChE Journal, 58(4), 986–997. http://doi.org/10.1002/aic.13734
dc.relation.referencesReading, N. C., & Sperandio, V. (2006). Quorum sensing: the many languages of bacteria. FEMS Microbiology Letters, 254(1), 1–11. http://doi.org/10.1111/j.1574-6968.2005.00001.x
dc.relation.referencesRen, Q., Chen, K., & Paulsen, I. T. (2007). TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Research, 35(Database issue), D274-9. http://doi.org/10.1093/nar/gkl925
dc.relation.referencesRen, Q., Kang, K. H., & Paulsen, I. T. (2004). TransportDB: a relational database of cellular membrane transport systems. Nucleic Acids Research, 32(Database issue), D284-8. http://doi.org/10.1093/nar/gkh016
dc.relation.referencesRingel, M. T., Dräger, G., & Brüser, T. (2016). PvdN Enzyme Catalyzes a Periplasmic Pyoverdine Modification. Journal of Biological Chemistry, 291(46), 23929–23938. http://doi.org/10.1074/jbc.M116.755611
dc.relation.referencesRomero, P., & Karp, P. (2003). PseudoCyc, A pathway-genome database for Pseudomonas aeruginosa. Journal of Molecular Microbiology and Biotechnology, 5(4), 230–239. http://doi.org/10.1159/000071075
dc.relation.referencesSaier, M. H. (2006). TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Research, 34(90001), D181–D186. http://doi.org/10.1093/nar/gkj001
dc.relation.referencesSchellenberger, J., Lewis, N. E., & Palsson, B. Ø. (2011). Elimination of Thermodynamically Infeasible Loops in Steady-State Metabolic Models. Biophysical Journal, 100(3), 544–553. http://doi.org/10.1016/j.bpj.2010.12.3707
dc.relation.referencesSchertzer, J. W., Brown, S. A., & Whiteley, M. (2010). Oxygen levels rapidly modulate Pseudomonas aeruginosa social behaviours via substrate limitation of PqsH. Molecular Microbiology, 77(6), 1527–1538. http://doi.org/10.1111/j.1365-2958.2010.07303.x
dc.relation.referencesSchuster, M., & Peter Greenberg, E. (2006a). A network of networks: Quorum-sensing gene regulation in Pseudomonas aeruginosa. International Journal of Medical Microbiology, 296(2–3), 73–81. http://doi.org/10.1016/j.ijmm.2006.01.036
dc.relation.referencesSchuster, M., & Peter Greenberg, E. (2006b). A network of networks: Quorum-sensing gene regulation in Pseudomonas aeruginosa. International Journal of Medical Microbiology, 296(2–3), 73–81. http://doi.org/10.1016/j.ijmm.2006.01.036
dc.relation.referencesSenger, R. S., & Papoutsakis, E. T. (2008). Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis. Biotechnology and Bioengineering, 101(5), 1036–1052. http://doi.org/10.1002/bit.22010
dc.relation.referencesSenger, R. S., Yen, J. Y., & Fong, S. S. (2014). A review of genome-scale metabolic flux modeling of anaerobiosis in biotechnology. Current Opinion in Chemical Engineering, 6, 33–42. http://doi.org/10.1016/j.coche.2014.08.003
dc.relation.referencesSerrano-Bermúdez, L. M. (2016). ANÁLISIS DE BALANCE DE FLUJO DINÁMICO DE LA PRODUCCIÓN DE 1,3 - PROPANODIOL A PARTIR DE Clostridium sp. Universidad Nacional de Colombia. Universidad Nacional de Colombia.
dc.relation.referencesShapiro, J. a. (1998). Thinking about bacterial populations as multicellular organisms. Annual Review of Microbiology, 52, 81–104. http://doi.org/10.1146/annurev.micro.52.1.81
dc.relation.referencesSilva, G. A. Da, & Almeida, E. A. De. (2006). Production of yellow-green fluorescent pigment by Pseudomonas fluorescens. Brazilian Archives of Biology and Technology, 49(3), 411–419. http://doi.org/10.1590/S1516-89132006000400009
dc.relation.referencesSkaar, E. P. (2010). The battle for iron between bacterial pathogens and their vertebrate hosts. PLoS Pathogens, 6(8), e1000949. http://doi.org/10.1371/journal.ppat.1000949
dc.relation.referencesStover, C. K., Pham, X. Q., Erwin, a L., Mizoguchi, S. D., Warrener, P., Hickey, M. J., ... Olson, M. V. (2000). Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen. Nature, 406(6799), 959–64. http://doi.org/10.1038/35023079
dc.relation.referencesSumner, T., Shephard, E., & Bogle, I. D. L. (2012). A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling. Journal of The Royal Society Interface, 9(74), 2156–2166. http://doi.org/10.1098/rsif.2011.0891
dc.relation.referencesTaffi, M., Paoletti, N., Angione, C., Pucciarelli, S., Marini, M., & LiÃ2, P. (2014). Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis. Frontiers in Genetics, 5, 319. http://doi.org/10.3389/fgene.2014.00319
dc.relation.referencesUniProt: the universal protein knowledgebase. (2017). Nucleic Acids Research, 45(D1), D158– D169. http://doi.org/10.1093/nar/gkw1099
dc.relation.referencesVan Riel, N. A. W., Giuseppin, M. L. F., & Verrips, C. T. (2000). Dynamic Optimal Control of Homeostasis: An Integrative System Approach for Modeling of the Central Nitrogen Metabolism in Saccharomyces cerevisiae. Metabolic Engineering, 2(1), 49–68. http://doi.org/10.1006/mben.1999.0137
dc.relation.referencesVasil, M. L., Ochsner, U. A., Johnson, Z., Colmer, J. A., & Hamood, A. N. (1998). The Fur- regulated gene encoding the alternative sigma factor PvdS is required for iron-dependent expression of the LysR-type regulator PtxR in pseudomonas aeruginosa. Journal of Bacteriology, 180(24), 6784–6788.
dc.relation.referencesVerhulst, P. F. (1845). Recherches mathématiques sur la loi d’accroissement de la population. Nouveaux Mémoires de l’Académie Royale Des Sciences et Belles-Lettres de Bruxelles, 18, 14–54. Retrieved from https://eudml.org/doc/182533
dc.relation.referencesVerhulst, P. F. (1847). Deuxième Mémoire sur la Loi d’Accroissement de la Population. Mémoires de l’Académie Royale Des Sciences, Des Lettres et Des Beaux-Arts de Belgique, 20, 1–32. Retrieved from https://eudml.org/doc/178976
dc.relation.referencesVert, J. P. (2010). Reconstruction of Biological Networks by Supervised Machine Learning Approaches. Elements of Computational Systems Biology, 165–188. http://doi.org/10.1002/9780470556757.ch7
dc.relation.referencesVeselova, M. A. (2010). Quorum sensing regulation in Pseudomonas. Russian Journal of Genetics, 46(2), 129–137. http://doi.org/10.1134/S1022795410020018
dc.relation.referencesVisca, P., Ciervo, A., & Orsi, N. (1994). Cloning and nucleotide sequence of the pvdA gene encoding the pyoverdine biosynthetic enzyme L-ornithine N5-oxygenase in Pseudomonas aeruginosa. J. Bacteriol., 176(4), 1128–1140. Retrieved from http://jb.asm.org/cgi/content/abstract/176/4/1128
dc.relation.referencesVisca, P., Imperi, F., & Lamont, I. L. (2007a). Pyoverdine siderophores: from biogenesis to biosignificance. Trends in Microbiology, 15(1), 22–30. http://doi.org/10.1016/j.tim.2006.11.004
dc.relation.referencesVisca, P., Imperi, F., & Lamont, I. L. (2007b). Pyoverdine Synthesis and its Regulation in Fluorescent Pseudomonads. In A. Varma & S. Chincholkar (Eds.), Microbial Siderophores (Vol. 12, pp. 135–163). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-71160-5_7
dc.relation.referencesVoulhoux, R., Filloux, A., & Schalk, I. J. (2006). Pyoverdine-mediated iron uptake in Pseudomonas aeruginosa: The Tat system is required for PvdN but not for FpvA transport. Journal of Bacteriology, 188(9), 3317–3323. http://doi.org/10.1128/JB.188.9.3317- 3323.2006
dc.relation.referencesWade, D. S., Calfee, M. W., Rocha, E. R., Ling, a, Engstrom, E., Coleman, J. P., ... Pesci, E. C. (2005). Regulation of Pseudomonas quinolone signal synthesis in Pseudomonas aeruginosa. Journal of Bacteriology, 187(13), 4372–4380. http://doi.org/10.1128/JB.187.13.4372
dc.relation.referencesWang, S., Wang, Y., Du, W., Sun, F., Wang, X., Zhou, C., & Liang, Y. (2007). A multi-approaches- guided genetic algorithm with application to operon prediction. Artificial Intelligence in Medicine, 41(2), 151–159. http://doi.org/10.1016/J.ARTMED.2007.07.010
dc.relation.referencesWaters, C. M., & Bassler, B. L. (2005). QUORUM SENSING: Cell-to-Cell Communication in Bacteria. Annual Review of Cell and Developmental Biology, 21(1), 319–346. http://doi.org/10.1146/annurev.cellbio.21.012704.131001
dc.relation.referencesWesterhoff, H. V., Winder, C., Messiha, H., Simeonidis, E., Adamczyk, M., Verma, M., ... Dunn, W. (2009). Systems Biology: The elements and principles of Life. FEBS Letters, 583(24), 3882–3890. http://doi.org/10.1016/j.febslet.2009.11.018
dc.relation.referencesYamanishi, Y., Mihara, H., Osaki, M., Muramatsu, H., Esaki, N., Sato, T., ... Kanehisa, M. (2007). Prediction of missing enzyme genes in a bacterial metabolic network. FEBS Journal, 274(9), 2262–2273. http://doi.org/10.1111/j.1742-4658.2007.05763.x
dc.relation.referencesYeterian, E., Martin, L. W., Guillon, L., Journet, L., Lamont, I. L., & Schalk, I. J. (2010). Synthesis of the siderophore pyoverdine in Pseudomonas aeruginosa involves a periplasmic maturation. Amino Acids, 38(5), 1447–1459. http://doi.org/10.1007/s00726-009-0358-0
dc.relation.referencesYuan, Q., Huang, T., Li, P., Hao, T., Li, F., Ma, H., ... Goryanin, I. (2017). Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models. PLOS ONE, 12(1), e0169437. http://doi.org/10.1371/journal.pone.0169437
dc.relation.referencesZhao, J.-K., Li, X.-M., Ai, G.-M., Deng, Y., Liu, S.-J., & Jiang, C.-Y. (2016). Reconstruction of metabolic networks in a fluoranthene-degrading enrichments from polycyclic aromatic hydrocarbon polluted soil. Journal of Hazardous Materials, 318, 90–98. http://doi.org/10.1016/j.jhazmat.2016.06.055
dc.relation.referencesZi, Z. (2011). Sensitivity analysis approaches applied to systems biology models. IET Systems Biology, 5(6), 336–6. http://doi.org/10.1049/iet-syb.2011.0015
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalQuorum-Sensing
dc.subject.proposalReconstrucción de redes metabólicas
dc.subject.proposalPseudomonas aeruginosa
dc.subject.proposalPioverdina
dc.subject.proposalAnálisis de balance de flujo
dc.subject.proposalMetabolic network reconstruction
dc.subject.proposalPyoverdine
dc.subject.proposalFlux balance analysis
dc.subject.unescoGenoma
dc.subject.unescoGenome
dc.subject.unescoBiología celular
dc.subject.unescoCell biology
dc.title.translatedReconstrução, modelagem e simulação da rede metabólica e Quorum-Sensing envolvidas na regulação de um fenótipo específico em Pseudomonas aeruginosa
dc.title.translatedReconstruction, modeling and simulation of the metabolic and Quorum-Sensing network involved in the regulation of a specific phenotype in Pseudomonas aeruginosa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TD
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
oaire.awardtitleReconstrucción, modelamiento y simulación de la red metabólica y de Quorum-Sensing, implicadas en la regulación de un fenotipo específico en Pseudomonas aeruginosa
oaire.fundernameMinisterio de Ciencia y Tecnologóa
dcterms.audience.professionaldevelopmentInvestigadores


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