Modelación de sistemas biológicos metaestables en la mesoescala

dc.contributor.advisorHernández Ortiz, Juan Pablo
dc.contributor.authorMaldonado Perez, Daniel Oswaldo
dc.contributor.researchgroupCrs-Tid Center for Research and Surveillance of Tropical and Infectious Diseasesspa
dc.date.accessioned2022-09-01T13:43:54Z
dc.date.available2022-09-01T13:43:54Z
dc.date.issued2022-05-28
dc.descriptionilustraciones, diagramas, tablasspa
dc.description.abstractModelación de sistemas biológicos metaestables en la mesoescala Este trabajo presenta diferentes métodos y plataformas de entendimientos de procesos biológicos como proteínas, complejos supramoleculares y enfermedades trasmitidas por vectores como dengue en diferentes escalas. A nivel de proteínas, se emplea métodos atomísticos de dinámica molecular para analizar la evolución de proteínas en agua, para luego medir propiedades fisicoquímicas en el tiempo y así extraer las superficies en un estado metaestable de cada una de ellas. La formación de estructuras supramoleculares a través de agregaciones o segregaciones en sistema biológicos entre diferentes moléculas se da principalmente por interacciones electrostáticas en la escala mesoscópica, por consiguiente, se estudia modelos como Monte Carlo y dinámica molecular permiten entender el comportamiento energético y de interacción de las configuraciones del sistema como las formaciones de hélices. Por último, análisis retrospectivo de rezagos entre diferentes variables climáticas, fenómeno del niño, índices asociados al atlántico permite entender las incidencias de estas en casos de dengue El cálculo de la constante dieléctrica se puede ver afectada debido a las interacciones entre el agua y las proteínas como posibles efectos de polarización entre ellas. La energía libre de los sistemas helicoidales del sistema específico del disminuye a medida que aumenta la longitud de debye en modelo de Monte Carlo. Las principales variables que inciden o afectan en la aparición de casos del dengue son; el fenómeno del niño, índice del Caribe (CAR) y Noratlántico Tropical (NTA) debido a las fluctuaciones de temperaturas. (Texto tomado de la fuente)spa
dc.description.abstractThis work shows different methods and platforms for understanding biological processes such as proteins, supramolecular complexes, and vector-borne diseases at different scales. At the level of proteins, atomistic molecular dynamics methods are used to analyze the evolution of proteins in water, measure physicochemical properties over time and thus extract the surfaces in a metastable state of each of them. The formation of supramolecular structures through aggregations or segregations in biological systems between different molecules is mainly due to electrostatic interactions on the mesoscopic scale; therefore, models such as Monte Carlo and Molecular Dynamics are studied, allowing us to understand the energy and interaction behavior of molecules. System configurations such as helix formations. Finally, retrospective analysis of laps between different climatic variables, El Niño phenomenon, indices associated with the Atlantic allows us to understand the incidences of these in dengue cases. The calculation of the dielectric constant can be affected due to the interactions between water and proteins as possible polarization effects between them. The free energy of the helical systems of the specific system decreases as the Debye length increases in the Monte Carlo model. The main variables that influence or affect the appearance of dengue cases are the El Niño phenomenon, the Caribbean index (CAR), and the North Atlantic Tropical index (NTA) due to temperature fluctuations.eng
dc.description.curricularareaÁrea curricular de Ingeniería Química e Ingeniería de Petróleosspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Ingeniería - Ingeniería químicaspa
dc.description.researchareaBiofísicaspa
dc.description.researchareaBiología computacionalspa
dc.format.extent84 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/82231
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.departmentDepartamento de Procesos y Energíaspa
dc.publisher.facultyFacultad de Minasspa
dc.publisher.placeMedellín, Colombiaspa
dc.publisher.programMedellín - Minas - Maestría en Ingeniería - Ingeniería Químicaspa
dc.relation.referencesJ. Walpole, J. A. Papin, and S. M. Peirce, “Multiscale Computational Models of Complex Biological Systems,” Annu. Rev. Biomed. Eng., vol. 15, no. 1, pp. 137–154, 2013, doi: 10.1146/annurev-bioeng-071811-150104spa
dc.relation.referencesM. Tawhai, J. Bischoff, D. Einstein, A. Erdemir, T. Guess, and J. Reinbolt, “Multiscale modeling in computational biomechanics.,” IEEE Eng. Med. Biol. Mag., vol. 28, no. 3, pp. 41–9, 2009, doi: 10.1109/MEMB.2009.932489.spa
dc.relation.referencesJ. S. Yu and N. Bagheri, “Multi-class and multi-scale models of complex biological phenomena,” Curr. Opin. Biotechnol., vol. 39, pp. 167–173, 2016, doi: 10.1016/j.copbio.2016.04.002spa
dc.relation.referencesJ. O. Dada and P. Mendes, “Multi-scale modelling and simulation in systems biology,” Integr. Biol., vol. 3, no. 2, p. 86, 2011, doi: 10.1039/c0ib00075bspa
dc.relation.referencesG. A. Vásquez-Montoya, J. S. Danobeitia, L. A. Fernández, and J. P. Hernández-Ortiz, “Computational immuno-biology for organ transplantation and regenerative medicine,” Transplant. Rev., vol. 30, no. 4, pp. 235–246, 2016, doi: 10.1016/j.trre.2016.05.002.spa
dc.relation.referencesM. L. Martins, S. C. Ferreira, and M. J. Vilela, “Multiscale models for biological systems,” Curr. Opin. Colloid Interface Sci., vol. 15, no. 1–2, pp. 18–23, 2010, doi: 10.1016/j.cocis.2009.04.004spa
dc.relation.referencesC. T. S. Epistemus, “Métodos de simulación computacional en biología,” pp. 84–92.spa
dc.relation.referencesY. L. Wang, Y. L. Zhu, Z. Y. Lu, and A. Laaksonen, “Electrostatic interactions in soft particle systems: Mesoscale simulations of ionic liquids,” Soft Matter, vol. 14, no. 21, pp. 4252–4267, 2018, doi: 10.1039/c8sm00387d.spa
dc.relation.referencesH. X. Zhou and X. Pang, “Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation,” Chem. Rev., vol. 118, no. 4, pp. 1691–1741, 2018, doi: 10.1021/acs.chemrev.7b00305.spa
dc.relation.referencesP. Kuki and J. E. Nielsen, “Electrostatics in proteins and protein-ligand complexes,” Future Med. Chem., vol. 2, no. 4, pp. 647–666, 2010, doi: 10.4155/fmc.10.6.spa
dc.relation.referencesM. Lund and B. Jönsson, “A Mesoscopic Model for Protein-Protein Interactions in Solution,” Biophys. J., vol. 85, no. 5, pp. 2940–2947, 2003, doi: 10.1016/S0006-3495(03)74714-6spa
dc.relation.referencesC. E. Dykstra, “Electrostatic Interaction Potentials in Molecular Force Fields,” Chem. Rev., vol. 93, no. 7, pp. 2339–2353, 1993, doi: 10.1021/cr00023a001spa
dc.relation.referencesJ. P. Hernández-Ortiz, J. J. de Pablo, and M. D. Graham, “N log N method for hydrodynamic interactions of confined polymer systems: Brownian dynamics.,” J. Chem. Phys., vol. 125, no. 16, p. 164906, 2006, doi: 10.1063/1.2358344.spa
dc.relation.referencesM. M. Gromiha, R. Nagarajan, and S. Selvaraj, “Protein Structural Bioinformatics: An Overview,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, ppspa
dc.relation.referencesS. Skariyachan and S. Garka, “Exploring the binding potential of carbon nanotubes and fullerene towards major drug targets of multidrug resistant bacterial pathogens and their utility as novel therapeutic agents,” Fullerenes, Graphenes Nanotub. A Pharm. Approach, pp. 1–29, Jan. 2018, doi: 10.1016/B978-0-12-813691-1.00001-4spa
dc.relation.referencesO. Sensoy, J. G. Almeida, J. Shabbir, I. S. Moreira, and G. Morra, “Computational studies of G protein-coupled receptor complexes: Structure and dynamics,” Methods Cell Biol., vol. 142, pp. 205–245, Jan. 2017, doi: 10.1016/BS.MCB.2017.07.011spa
dc.relation.referencesS. Abeln, K. A. Feenstra, and J. Heringa, “Protein Three-Dimensional Structure Prediction,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, pp. 497–511, Jan. 2019, doi: 10.1016/B978-0-12-809633-8.20505-0spa
dc.relation.referencesA. Šali and T. L. Blundell, “Comparative Protein Modelling by Satisfaction of Spatial Restraints,” J. Mol. Biol., vol. 234, no. 3, pp. 779–815, Dec. 1993, doi: 10.1006/JMBI.1993.1626.spa
dc.relation.referencesY. S. Watanabe, Y. Fukunishi, and H. Nakamura, “1P047 Modeling of Loops in Protein Structures,” Seibutsu Butsuri, vol. 44, no. supplement, p. S41, 2004, doi: 10.2142/biophys.44.s41_3.spa
dc.relation.referencesPoeran, “乳鼠心肌提取 HHS Public Access,” Physiol. Behav., vol. 176, no. 12, pp. 139–148, 2017, doi: 10.1002/cpbi.3.Comparativespa
dc.relation.referencesJ. J. Wendoloski and J. B. Matthew, “Molecular dynamics effects on protein electrostatics,” Proteins Struct. Funct. Bioinforma., vol. 5, no. 4, pp. 313–321, 1989, doi: 10.1002/prot.340050407.spa
dc.relation.referencesLindahl, Abraham, Hess, and van der Spoel, “GROMACS Documentation,” GROMACS 2021.3 Man., pp. 1–623, 2021.spa
dc.relation.referencesMax Planck Institute, “Atomistic Simulation of Biomolecular function,” Leonard Heinz. http://www2.mpibpc.mpg.de/groups/grubmueller/Lugano_Tutorial/part1/.spa
dc.relation.referencesM. P. Allen and D. J. Tildesley, “Computer simulation of liquids: Second edition,” Comput. Simul. Liq. Second Ed., pp. 1–626, 2017, doi: 10.1093/oso/9780198803195.001.0001spa
dc.relation.referencesT. T. Nguyen, M. H. Viet, and M. S. Li, “Effects of water models on binding affinity: Evidence from all-atom simulation of binding of tamiflu to A/H5N1 neuraminidase,” Sci. World J., vol. 2014, 2014, doi: 10.1155/2014/536084spa
dc.relation.referencesA. Emperador, R. Crehuet, and E. Guàrdia, “Effect of the water model in simulations of protein–protein recognition and association,” Polymers (Basel)., vol. 13, no. 2, pp. 1–9, 2021, doi: 10.3390/polym13020176.spa
dc.relation.referencesI. Snook, “Brownian Dynamics,” Langevin Gen. Langevin Approach to Dyn. At. Polym. Colloid. Syst., pp. 133–156, 2007, doi: 10.1016/b978-044452129-3/50008-0.spa
dc.relation.referencesR. R. Gabdoulline and R. C. Wade, “METHODS: A Companion to Methods in Brownian Dynamics Simulation of Protein–Protein Diffusional Encounter,” Enzymology, vol. 14, pp. 329–341, 1998, [Online]. Available: https://dasher.wustl.edu/chem478/labs/lab-11/methods-14-329-98.pdfspa
dc.relation.referencesS. Palacios, V. Romero-Rochin, and K. Volke-Sepulveda, “Brownian motion in typical microparticle systems,” pp. 1–12, 2011, [Online]. Available: http://arxiv.org/abs/1108.3316.spa
dc.relation.referencesP. Debye and E. Huckel, “The theory of electrolytes I. The lowering of the freezing point and related occurrences,” Phys. Zeitschrift, vol. 24, no. 1923, pp. 185–206, 1923.spa
dc.relation.referencesA. Vasquez Echeverri, “Solución de la ecuación de Fokker-Plank para simular ADN confinado fuera del equilibrio considerando condiciones electrostaticas,” p. 94, 2016, [Online]. Available: http://www.bdigital.unal.edu.co/55094/.spa
dc.relation.referencesE. Hückel, “Zur Theorie der Elektrolyte,” Ergebnisse der exakten naturwissenschaften, pp. 199–276, 2007, doi: 10.1007/bfb0111753.spa
dc.relation.referencesS. R. Barnum and T. N. Schein, The Complement System, Second Edi. Elsevier Ltd, 2018.spa
dc.relation.referencesJ. Laskowski and J. M. Thurman, Factor B, Second Edi. Elsevier Ltd, 2018.spa
dc.relation.referencesS. R. Barnum, C3, Second Edi. 2018spa
dc.relation.referencesB. J. C. Janssen et al., “Structures of complement component C3 provide insights into the function and evolution of immunity,” Nature, vol. 437, no. 7058, pp. 505–511, 2005, doi: 10.1038/nature04005.spa
dc.relation.referencesW. DeLano and S. Bromberg, “PyMOL User’s Guide (Original),” DeLano Sci. LLC, pp. 1–66, 2004, [Online]. Available: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:PyMOL+User’s+Guide#2.spa
dc.relation.referencesC. User, “Chimera1.10_UsersGuide.”spa
dc.relation.referencesD. M. Hinckley, G. S. Freeman, J. K. Whitmer, and J. J. De Pablo, “An experimentally-informed coarse-grained 3-site-per-nucleotide model of DNA: Structure, thermodynamics, and dynamics of hybridization,” J. Chem. Phys., vol. 139, no. 14, 2013, doi: 10.1063/1.4822042.spa
dc.relation.referencesC. Eno, C. L. Hansen, and F. Pelegri, “Aggregation, segregation, and dispersal of homotypic germ plasm RNPs in the early zebrafish embryo,” Dev. Dyn., vol. 248, no. 4, pp. 306–318, 2019, doi: 10.1002/dvdy.18.spa
dc.relation.referencesY. Hashimoto et al., “Localized maternal factors are required for zebrafish germ cell formation,” Dev. Biol., vol. 268, no. 1, pp. 152–161, 2004, doi: 10.1016/j.ydbio.2003.12.013.spa
dc.relation.referencesT. Trcek, M. Grosch, A. York, H. Shroff, T. Lionnet, and R. Lehmann, “Drosophila germ granules are structured and contain homotypic mRNA clusters,” Nat. Commun., vol. 6, 2015, doi: 10.1038/ncomms8962.spa
dc.relation.referencesC. Eno and F. Pelegri, “Gradual recruitment and selective clearing generate germ plasm aggregates in the zebrafish embryo,” Bioarchitecture, vol. 3, no. 4, pp. 125–132, 2013, doi: 10.4161/bioa.26538.spa
dc.relation.referencesS. Nijjar and H. R. Woodland, “Protein interactions in Xenopus germ plasm RNP particles,” PLoS One, vol. 8, no. 11, 2013, doi: 10.1371/journal.pone.0080077.spa
dc.relation.referencesC. Yoon, K. Kawakami, and N. Hopkins, “Zebrafish vasa homologue RNA is localized to the cleavage planes of 2- and 4-cell-stage embryos and is expressed in the primordial germ cells.,” Development, vol. 124, no. 16, pp. 3157–65, 1997, [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/9272956.spa
dc.relation.referencesJ. A. Toretsky and P. E. Wright, “Assemblages: Functional units formed by cellular phase separation,” J. Cell Biol., vol. 206, no. 5, pp. 579–588, 2014, doi: 10.1083/jcb.201404124.spa
dc.relation.referencesC. P. Brangwynne, P. Tompa, and R. V. Pappu, “Polymer physics of intracellular phase transitions,” Nat. Phys., vol. 11, no. 11, pp. 899–904, 2015, doi: 10.1038/nphys3532spa
dc.relation.referencesY. Lin, D. S. W. Protter, M. K. Rosen, and R. Parker, “Formation and Maturation of Phase-Separated Liquid Droplets by RNA-Binding Proteins,” Mol. Cell, vol. 60, no. 2, pp. 208–219, 2015, doi: 10.1016/j.molcel.2015.08.018spa
dc.relation.referencesTwo segrega8on pathways of germ plasm RNPs in teleosts.”spa
dc.relation.referencesM. Rouhani, F. Khodabakhsh, D. Norouzian, R. A. Cohan, and V. Valizadeh, “Molecular dynamics simulation for rational protein engineering: Present and future prospectus,” J. Mol. Graph. Model., vol. 84, pp. 43–53, 2018, doi: 10.1016/j.jmgm.2018.06.009spa
dc.relation.referencesA. Sasse, K. U. Laverty, T. R. Hughes, and Q. D. Morris, “Motif models for RNA-binding proteins,” Curr. Opin. Struct. Biol., vol. 53, no. August, pp. 115–123, 2018, doi: 10.1016/j.sbi.2018.08.001.spa
dc.relation.referencesC. Cragnell, E. Rieloff, and M. Skepö, “Utilizing Coarse-Grained Modeling and Monte Carlo Simulations to Evaluate the Conformational Ensemble of Intrinsically Disordered Proteins and Regions,” J. Mol. Biol., vol. 430, no. 16, pp. 2478–2492, 2018, doi: 10.1016/j.jmb.2018.03.006.spa
dc.relation.referencesC. Rodriguez. and M. Aluztisa., “Medicina molecular de FIBAO,” Medicina (B. Aires)., vol. 1, pp. 7–9, 2007, [Online]. Available: www.medmol.es.spa
dc.relation.referencesCDC, “Centro Nacional para Enfermedades Infecciosas Emergentes y Zoonóticas División de Enfermedades Transmitidas por Vectores,” J. Infect. Dis., vol. 41, no. 2, p. 1, 2020, [Online]. Available:spa
dc.relation.referencesNOOA, “El Niño Regions,” 2005, [Online]. Available: https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions.shtml.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/spa
dc.subject.ddc540 - Química y ciencias afinesspa
dc.subject.ddc610 - Medicina y salud::613 - Salud y seguridad personalspa
dc.subject.lembEnfermedades transmitidas por vectoresspa
dc.subject.lembDenguespa
dc.subject.proposalProteínasspa
dc.subject.proposalSuperficiesspa
dc.subject.proposalMolecularspa
dc.subject.proposalAgregaciónspa
dc.subject.proposalProteinseng
dc.subject.proposalSurfaceseng
dc.subject.proposalMoleculareng
dc.subject.proposalAggregationeng
dc.titleModelación de sistemas biológicos metaestables en la mesoescalaspa
dc.title.translatedModeling of metastable biological systems at the mesoscaleeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.fundernameMinCiencias Convocatoria 807-2018spa

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