Abadi, Noam (2021) Modelos de selección natural basados en energía / Energy-based natural selection models. Maestría en Ciencias Físicas, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
Desde el trabajo de Darwin, El origen de las especies, la selección natural (supervivencia de los más aptos) ha sido cada vez más aceptado como el mecanismo por el cual las especies evolucionan y se extinguen. A pesar de su creciente popularidad, no hay un consenso general acerca de cómo determinar cuáles son, realmente, las especies más aptas. Para solucionar esto, proponemos una manera de estudiar la selección natural que no se basa en buscar una función a optimizar sino en imponer una restricción fundada en las necesidades energéticas de los seres vivos. Nuestro modelo captura la forma general en la que las características fisiológicas (fenotipo) codificadas en el ADN (genotipo) determinan el balance entre sus costos y retribuciones. De generación en generación, los parámetros de esas restricciones se mutan aleatoriamente, y los individuos no tienen ningún objetivo en particular. Ni siquiera el de sobrevivir está impuesto como un objetivo en sí, sino que lo hacen los que pueden. Al incorporar una competencia por una fuente de energía compartida (recurso) surge la noción de aptitud genética en la supervivencia reiterada de individuos con ciertos rasgos comunes. Estudiamos algunos casos particulares de la dinámica energética que surge del balance entre costo y retribución. En primera instancia, cadenas booleanas cuyos bits interpretamos como genes activos o inactivos (y asociados con un consumo, retribución máxima, y un estado booleano sucesivo de cada individuo) sirvieron para estudiar distintas dinámicas de recuperación de recurso en “seres vivos” más similares a moléculas prebióticas. Luego, para estudiar la transmisión de información genética, nos deshicimos de los estados booleanos y tratamos con un consumo, retribución máxima y tasa de reproducción constantes para cada individuo, permitiendo definir una “distancia” genética de manera más objetiva. En una tercera etapa, consideramos el costo (principalmente metabólico y cinético) que tiene asociado un cuerpo de cierto tamaño, pudiendo moverse a una velocidad limitada por el mismo. Finalmente, agregamos un grado de libertad al modelo anterior, con la velocidad independiente al tamaño, para producir un escenario más realista. Hicimos variaciones a los diferentes casos y observamos cómo estas influían en los resultados ya reportados. Hallamos que muchos de los resultados conocidos de la biología teórica y cuantitativa surgen en este tipo de modelos sin incorporarlos explícitamente. Estos incluyen pero no se limitan a la exclusión competitiva, el equilibrio puntuado, la sensibilidad a condiciones iniciales, la convergencia evolutiva, la especiación, y el altruismo grupal a pesar de reglas egoístas individuales.
Resumen en inglés
Ever since Darwin’s The origin of species, natural selection (survival of the fittest) has been more and more accepted as the pathway through which species evolve and go extinct. Despite its growing popularity, there is no general concensus in determining which are, in fact, the fittest species. To face this problem, we propose a way to study natural selection without imposing a function to be optimized, but a restriction given by energetic necessities of living beings. Our model captures the way in which physiological traits (the phenotype) encoded by the DNA (genotype) determine the balance between costs and retributions of said traits. From one generation to the next, the parameters involved in the restrictions mutate randomly, with no general goal implied for individuals. Not even survival is imposed as a goal, since the ones that do so are merely the ones that can. Through competition for a shared energy source (resource), a notion of fitness emerges in the repeated survival of individuals with certain common traits. We study some special cases of the energy dynamics that result from the balance between cost and retribution. In a first scenario, we use boolean chains whose bits we interpret as active or inactive genes (associated with an energy consumption, maximum retribution and following boolean state for each individual) to study different forms of resource recovery in “living beings” more similar to prebiotic molecules. Next, we drop the boolean states and focous only on consumption, retribution and reproduction frequency to get a clearer idea of the relation between genetic and phenotypic similarity, and the influence of transmiting genetic information. In a third stage, we devise a model for the metabolic and kinetic cost of an organism of a certain body size, with an associated velocity limited by said size. Finally, we add a degree of freedom to the former model, uncoupling velocity from body size to produce a more realistic scenario. We also vary the different cases, observing how these changes influence reported results. With this method, we have found that many of the known results of theoretical and quantitative biology emerge without their explicit inception in the model. These include (but are not limited to) competitive exclusion, punctuated equilibrium, sensitivity to initial conditions, evolutionary convergence, speciation and group altruism from selfish individual rules.
| Tipo de objeto: | Tesis (Maestría en Ciencias Físicas) |
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| Palabras Clave: | Biology; Biología; Statistics; Estadística; Energy; Energía; [Fitness; Computer models; Modelos computacionales; Natural selection; Selección natural] |
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Redes neuronales para la búsqueda de estrategias óptimas en problemas de econofísica, Febrero 2021. Tesis de Maestría en Ciencias Físicas. Instituto Balseiro, Universidad Nacional de Cuyo. |
| Materias: | Física > Física estadística |
| Divisiones: | Gcia. de área de Investigación y aplicaciones no nucleares > Gcia. de Física > Sistemas complejos y altas energías > Física estadística interdisciplinaria |
| Código ID: | 932 |
| Depositado Por: | Marisa G. Velazco Aldao |
| Depositado En: | 05 Jul 2021 11:08 |
| Última Modificación: | 05 Jul 2021 12:45 |
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