Prado , Ayelén (2018) Evolucion biológica en el origen de la vida: transmisión de genes horizontal versus vertical. / Evolution in the origins of live: horizontal vesus vertical gene transfer. Maestría en Ciencias Físicas, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
Una de las teorías más aceptadas hoy en día sobre el origen de la vida y de la evolución biológica, basada en las ideas de C. R. Woese, indica que la vida comenzó con una comunidad de organismos microscópicos que tenían la capacidad, no sólo de replicarse, sino de intercambiar material genético entre individuos mediante la llamada transmisión horizontal de genes (THG), que hoy se observa principalmente en algunos tipos de bacterias. (Por contraposición, la transmisión de genes de padres a hijos se llama vertical.) En esta tesis estudiamos y desarrollamos modelos matemáticos para la evolución en condiciones de coexistencia de transmisión horizontal y vertical de genes. El objetivo apunta a contribuir a entender cómo pudo haberse dado la transición que llevó de una etapa de evolución biológica en el que primaba la THG, a una en la que prima la transmisión vertical. El modelo base considerado para la evolución de la población de progenotes incluye los efectos de la reproducción asexual, las mutaciones puntuales y la THG. Analizamos dos maneras diferentes de generar la dinámica evolutiva: una estocástica basada en el algoritmo de Gillespie y una determinista. Estudiamos los cambios en la dinámica en función de los distintos parámetros del sistema y de las reglas aplicadas para seleccionar las conexiones de THG. Mediante el algoritmo de Gillespie vimos que, cuando la tasa de THG es alta, el sistema es biestable, alternando periódicamente entre configuraciones de alta y baja entropía. Esta biestabilidad no se observa con el modelo determinista, lo cual sugiere que es producto de las fluctuaciones, y resalta la relevancia de la estocasticidad. El principal resultado obtenido, y verificado tanto en la dinámica estocástica como en la determinista, indica que la THG puede contribuir a acelerar el proceso por el cual emerge (sobrevive) una única especie predominante (la de mejor adaptación al ambiente), con tal de que los genotipos mejor adaptados están poco predispuestos a recibir material genético externo.
Resumen en inglés
One of the most widely accepted theories on the origin of life and biological evolution is based on the ideas of C. R. Woose, and states that life began with a community of microscopic organisms which had both the ability of self-replication and the of exchanging genetic material between themselves by means of horizontal gene transfer (HGT). Nowadays HGT still takes place for some types of bacteria. By contraposition, the transfer of genetic material from parent to offspring is denoted vertical gene transfer. In this thesis, we have developed and studied a mathematical model for evolution in the case of coexistence of horizontal and vertical gene transfer, aiming to contribute to the understanding of the means by which the transition from a stage in evolution with rampant HGT to a stage of predominance of vertical gene transfer could have taken place. Our base model for the evolution of a community of progenotes includes the effects of asexual reproduction, point mutations and HGT. The dynamics of the system were analyzed in two ways: the rst, stochastic based on the Gillespie algorithm, and the other deterministic. We studied the change in the dynamics as a function of the system parameters and of the procedure used to select the HGT connections. Results obtained by the Gillespie algorithm showed that the system presents a bistabilty when HGT is frequent, transitioning periodically between high and low entropy congurations. The bistability was not obtained with the deterministic model, suggesting that it is due to stochastic fructuations. We also found that, if we consider that well adapted genotypes are little predisposed to receiving external genetic material, HGT accelerates the process by which the species best adapted to the environment emerges.
| Tipo de objeto: | Tesis (Maestría en Ciencias Físicas) |
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| Palabras Clave: | Biological evolution; Evolución biológica; Genes; [Progenotes; Trasformation; Trasformación; Biological simulation; Simulación biológica] |
| Referencias: | [1] Arnoldt, H., Strogatz, S. H., Timme, M. Toward the darwinian transition: switching between distributed and speciated states in a simple model of early life. Physical Review E, 92 (5), 052909, 2015. viii, 4, 15, 20, 21, 22, 33, 34, 35, 37, 38, 39, 40, 42, 50, 51, 53, 63 [2] Wright, S. Evolution in mendelian populations. Genetics, 16 (2), 97, 1931. 2 [3] Doolittle, W. F. Uprooting the tree of life. Scientic American, 282 (2), 90-95, 2000. 2, 3 [4] Tatum, E., Lederberg, J. Gene recombination in the bacterium escherichia coli. Journal of bacteriology, 53 (6), 673, 1947. 2 [5] Kaplan, T. The role of horizontal gene transfer in antibiotic resistance. Eukaryon, 10, 80{81, 2014. 3 [6] Koonin, E. V., Makarova, K. S., Aravind, L. Horizontal gene transfer in prokaryotes: quantication and classication. Annual Reviews in Microbiology, 55 (1), 709-742, 2001. 3 [7] Huang, J. Horizontal gene transfer in eukaryotes: The weak-link model. Bioessays, 35 (10), 868-875, 2013. 3 [8] Woese, C. The universal ancestor. Proceedings of the national academy of Sciences, 95 (12), 6854-6859, 1998. 3, 4 [9] Lehninger, A. L., Nelson, D. L. Principles of biochemistry. En: Principles of Biochemistry. Worth Publisher, 2012. 8, 10 [10] Vertessy, G. Uracil in dna: error or signal? 8 [11] Ridley, M. Evolution. En: Evolution. Blackwell, 2003. 10 [12] Nowak, A. M. Evolutionary dynamics. Harvard University Press, 2006. 10, 25, 28 [13] Bolotin, E., Hershberg, R. Horizontally acquired genes are often shared between closely related bacterial species. Frontiers in Microbiology, 8, 1536, 2017. URL https://www.frontiersin.org/article/10.3389/fmicb.2017.01536. 11 [14] Thomas, C. M., Nielsen, K. M. Mechanisms of, and barriers to, horizontal gene transfer between bacteria. Nature reviews microbiology, 3 (9), 711, 2005. 11, 12 [15] Dubnau, D. Dna uptake in bacteria. Annual Reviews in Microbiology, 53 (1), 217-244, 1999. 12 [16] Boto, L. Horizontal gene transfer in evolution: facts and challenges. Proceedings of the Royal Society of London B: Biological Sciences, 277 (1683), 819-827, 2010. 13 [17] Kurland, C. G., Canback, B., Berg, O. G. Horizontal gene transfer: a critical view. Proceedings of the National Academy of Sciences, 100 (17), 9658-9662, 2003. [18] Gogarten, J. P., Townsend, J. P. Horizontal gene transfer, genome innovation and evolution. Nature Reviews Microbiology, 3 (9), 679, 2005. 13 [19] Hey, J. The mind of the species problem. Trends in Ecology & Evolution, 16 (7), 326-329, 2001. 14 [20] Mayden, R. L. A hierarchy of species concepts: the denouement in the saga of the species problem, 1997. 14 [21] Mallet, J. Species, concepts of. Encyclopedia of biodiversity, 5, 427-440, 2001. 14 [22] Doolittle, W. F., Papke, R. T. Genomics and the bacterial species problem. Ge- nome biology, 7 (9), 116, 2006. 14 [23] Drossel, B. Biological evolution and statistical physics. Advances in physics, 50 (2), 209-295, 2001. 15, 16 [24] Moran, P. A. P. Random processes in genetics. Mathematical Proceedings of the Cambridge Philosophical Society, 54 (1), 6071, 1958. 22 [25] Gillespie, D. T. Exact stochastic simulation of coupled chemical reactions. The journal of physical chemistry, 81 (25), 2340-2361, 1977. 23 [26] Gillespie, D. T. A rigorous derivation of the chemical master equation. Physica A: Statistical Mechanics and its Applications, 188 (1-3), 404-425, 1992. 23, 25 [27] Press, W. H. Numerical Recipes. Cambrige University Press, 2007. 24 [28] Hofbauer, J., Sigmund, K. Evolutionary Games and Population Dynamics. Cambridge University Press, 1998. URL https://books.google.com.ar/books?id= Xu-H0ClCHN8C. 26 [29] Cao, Y., Li, H., Petzold, L. Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. The journal of chemical physics, 121 (9), 4059-4067, 2004. 29 [30] Riedel, M. D., Bruck, J. Exact stochastic simulation of chemical reactions with cycle leaping, 2006. [31] Gibson, M. A., Bruck, J. Efficient exact stochastic simulation of chemical systems with many species and many channels. The journal of physical chemistry A, 104 (9), 1876-1889, 2000. 29 [32] Jenkins, D., Peterson, G. D. Accelerating the stochastic simulation algorithm. En: Symposium on Application Accelerators in High-Performance Computing, Urbana, IL. 2009. 31 [33] Mickael, N., Mestivier, D. An efficient gpu acceptance-rejection algorithm for the selection of the next reaction to occur for stochastic simulation algorithms. arXiv preprint arXiv:1404.0027, 2014. 31 [34] Sumiyoshi, K., Hirata, K., Hiroi, N., Funahashi, A. Acceleration of discrete stochastic biochemical simulation using gpgpu. Frontiers in physiology, 6, 42, 2015. 31 [35] Komarov, I., D'Souza, R. M. Accelerating the gillespie exact stochastic simulation algorithm using hybrid parallel execution on graphics processing units. PLoS One, 7 (11), e46693, 2012. 31 |
| Materias: | Física > Simulación biológica |
| Divisiones: | Investigación y aplicaciones no nucleares > Física > Física estadística |
| Código ID: | 756 |
| Depositado Por: | Tamara Cárcamo |
| Depositado En: | 03 Feb 2021 12:29 |
| Última Modificación: | 03 Feb 2021 12:29 |
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