Modelación de sistemas biológicos metaestables en la mesoescala
ilustraciones, diagramas, tablas
- Autores:
-
Maldonado Perez, Daniel Oswaldo
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82231
- Palabra clave:
- 540 - Química y ciencias afines
610 - Medicina y salud::613 - Salud y seguridad personal
Enfermedades transmitidas por vectores
Dengue
Proteínas
Superficies
Molecular
Agregación
Proteins
Surfaces
Molecular
Aggregation
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Modelación de sistemas biológicos metaestables en la mesoescala |
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Modeling of metastable biological systems at the mesoscale |
| title |
Modelación de sistemas biológicos metaestables en la mesoescala |
| spellingShingle |
Modelación de sistemas biológicos metaestables en la mesoescala 540 - Química y ciencias afines 610 - Medicina y salud::613 - Salud y seguridad personal Enfermedades transmitidas por vectores Dengue Proteínas Superficies Molecular Agregación Proteins Surfaces Molecular Aggregation |
| title_short |
Modelación de sistemas biológicos metaestables en la mesoescala |
| title_full |
Modelación de sistemas biológicos metaestables en la mesoescala |
| title_fullStr |
Modelación de sistemas biológicos metaestables en la mesoescala |
| title_full_unstemmed |
Modelación de sistemas biológicos metaestables en la mesoescala |
| title_sort |
Modelación de sistemas biológicos metaestables en la mesoescala |
| dc.creator.fl_str_mv |
Maldonado Perez, Daniel Oswaldo |
| dc.contributor.advisor.none.fl_str_mv |
Hernández Ortiz, Juan Pablo |
| dc.contributor.author.none.fl_str_mv |
Maldonado Perez, Daniel Oswaldo |
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Crs-Tid Center for Research and Surveillance of Tropical and Infectious Diseases |
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540 - Química y ciencias afines 610 - Medicina y salud::613 - Salud y seguridad personal |
| topic |
540 - Química y ciencias afines 610 - Medicina y salud::613 - Salud y seguridad personal Enfermedades transmitidas por vectores Dengue Proteínas Superficies Molecular Agregación Proteins Surfaces Molecular Aggregation |
| dc.subject.lemb.spa.fl_str_mv |
Enfermedades transmitidas por vectores Dengue |
| dc.subject.proposal.spa.fl_str_mv |
Proteínas Superficies Molecular Agregación |
| dc.subject.proposal.eng.fl_str_mv |
Proteins Surfaces Molecular Aggregation |
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ilustraciones, diagramas, tablas |
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2022 |
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2022-09-01T13:43:54Z |
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2022-09-01T13:43:54Z |
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2022-05-28 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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https://repositorio.unal.edu.co/handle/unal/82231 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/82231 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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J. 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-071811-150104 M. 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. J. 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.2016.04.002 J. 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/c0ib00075b G. 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. M. 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.2009.04.004 C. T. S. Epistemus, “Métodos de simulación computacional en biología,” pp. 84–92. Y. 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. H. 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. P. 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. M. 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)74714-6 C. E. Dykstra, “Electrostatic Interaction Potentials in Molecular Force Fields,” Chem. Rev., vol. 93, no. 7, pp. 2339–2353, 1993, doi: 10.1021/cr00023a001 J. 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. M. M. Gromiha, R. Nagarajan, and S. Selvaraj, “Protein Structural Bioinformatics: An Overview,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, pp S. 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/B978-0-12-813691-1.00001-4 O. 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.2017.07.011 S. 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/B978-0-12-809633-8.20505-0 A. 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. Y. 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. Poeran, “乳鼠心肌提取 HHS Public Access,” Physiol. Behav., vol. 176, no. 12, pp. 139–148, 2017, doi: 10.1002/cpbi.3.Comparative J. 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. Lindahl, Abraham, Hess, and van der Spoel, “GROMACS Documentation,” GROMACS 2021.3 Man., pp. 1–623, 2021. Max Planck Institute, “Atomistic Simulation of Biomolecular function,” Leonard Heinz. http://www2.mpibpc.mpg.de/groups/grubmueller/Lugano_Tutorial/part1/. M. 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/9780198803195.001.0001 T. 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: 10.1155/2014/536084 A. 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. I. 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. R. 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.pdf S. 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. P. 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. A. 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/. E. Hückel, “Zur Theorie der Elektrolyte,” Ergebnisse der exakten naturwissenschaften, pp. 199–276, 2007, doi: 10.1007/bfb0111753. S. R. Barnum and T. N. Schein, The Complement System, Second Edi. Elsevier Ltd, 2018. J. Laskowski and J. M. Thurman, Factor B, Second Edi. Elsevier Ltd, 2018. S. R. Barnum, C3, Second Edi. 2018 B. 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. W. 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. C. User, “Chimera1.10_UsersGuide.” D. 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. C. 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. Y. 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. T. 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. C. 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. S. 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. C. 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. J. 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. C. 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/nphys3532 Y. 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.2015.08.018 Two segrega8on pathways of germ plasm RNPs in teleosts.” M. 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.2018.06.009 A. 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. C. 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. 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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hernández Ortiz, Juan Pablo0d123ac4980de4343bdf5d61e0de6ad3Maldonado Perez, Daniel Oswaldo3de14421a6e6f7ebb6ca0264066944deCrs-Tid Center for Research and Surveillance of Tropical and Infectious Diseases2022-09-01T13:43:54Z2022-09-01T13:43:54Z2022-05-28https://repositorio.unal.edu.co/handle/unal/82231Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, tablasModelación 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)This 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.MaestríaMagister en Ingeniería - Ingeniería químicaBiofísicaBiología computacionalÁrea curricular de Ingeniería Química e Ingeniería de Petróleos84 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería QuímicaDepartamento de Procesos y EnergíaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín540 - Química y ciencias afines610 - Medicina y salud::613 - Salud y seguridad personalEnfermedades transmitidas por vectoresDengueProteínasSuperficiesMolecularAgregaciónProteinsSurfacesMolecularAggregationModelación de sistemas biológicos metaestables en la mesoescalaModeling of metastable biological systems at the mesoscaleTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMJ. 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Rev., vol. 30, no. 4, pp. 235–246, 2016, doi: 10.1016/j.trre.2016.05.002.M. 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.2009.04.004C. T. S. Epistemus, “Métodos de simulación computacional en biología,” pp. 84–92.Y. 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.H. 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.P. 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.M. Lund and B. 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Available: https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions.shtml.MinCiencias Convocatoria 807-2018EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-84675https://repositorio.unal.edu.co/bitstream/unal/82231/1/license.txtb577153cc0e11f0aeb5fc5005dc82d8aMD51ORIGINAL1095813963_2022.pdf1095813963_2022.pdfTesis de Maestría en Ingeniería - Ingeniería Químicaapplication/pdf3932401https://repositorio.unal.edu.co/bitstream/unal/82231/2/1095813963_2022.pdf8e65583410980c2ba3b0071edbf8ed11MD52THUMBNAIL1095813963_2022.pdf.jpg1095813963_2022.pdf.jpgGenerated Thumbnailimage/jpeg4274https://repositorio.unal.edu.co/bitstream/unal/82231/3/1095813963_2022.pdf.jpg33d521b8240554d98dd70cdc0f543fbbMD53unal/82231oai:repositorio.unal.edu.co:unal/822312024-08-11 01:06:01.732Repositorio Institucional Universidad Nacional de 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