masterThesis
Estratégias evolutivas aplicadas a redes de regulação gênicas artificiais
Fecha
2021-09-29Registro en:
MOREIRA, André Luiz de Lucena. Estratégias evolutivas aplicadas a redes de regulação gênicas artificiais. 2021. 34f. Dissertação (Mestrado em Bioinformática) - Instituto Metrópole Digital, Universidade Federal do Rio Grande do Norte, Natal, 2021.
Autor
Moreira, André Luiz de Lucena
Resumen
Gene regulatory networks (GRNs) influence the behavioral response of
individuals when subjected to different contexts, they also affect extremely important
processes for life, such as cell differentiation, metabolism and evolution.
Computational models of gene regulatory networks, associated with artificial
intelligence, enable us to create adaptable and context-independent solutions. In this
work, we simulate the evolution of GRNs, aiming to evaluate how environmental
variation and network growth events impact on the model's learning capacity. For this,
we created populations of individuals represented by artificial gene regulatory networks
(AGRNs), with physical characteristics and behaviors based on bacteria. We then
simulated these populations on the tasks: “Objective Orientation”, “Phototaxy” and
“Phototaxy with Obstacles”, evaluating how the events of single gene duplication,
whole genome duplication and context change affect population evolution. The results
indicated that a gradual increase in the complexity of the tasks performed is beneficial
for the evolution of the model. Furthermore, we have seen that larger gene regulatory
networks are needed for more complex tasks, with single-gene duplication being a
good evolutionary strategy for growing these networks, as opposed to full-genome
duplication. Studying how GRNs evolved in a biological environment allows us not only
to improve the computational models produced, but also to provide insights into
aspects and events that influenced the development of life on earth.