Simulação computacional e análise de um modelo fenotípico de evolução viral

• Autor(a) principal: Castro, Diogo [UNIFESP]
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UNIFESP
• Texto Completo: https://repositorio.unifesp.br/handle/11600/10085
• topic: Branching processes; Computer simulation; Quasispecies; Viral evolution; Viruses; Mutation; Processos de ramificação; Quasispecies; Simulação computacional; Evolução viral; Vírus; Mutação

A large amount of viruses of medical importance such as HIV, respiratory syncytial virus, the hepatitis C virus, influenza A (H1N1) and polio virus, has RNA genome. These viruses exhibit extremely high mutational rate, fast replicative kinetics, large population of particles and high genetic diversity. Manifested during the infectious process, these features allow the virus population to adapt quickly to dynamic environments, escape from the immune system, develop resistance to vaccines and antiviral drugs, and display complex evolutionary dynamics whose understanding represents a challenge to the traditional population genetics and for effective therapeutic intervention strategies. To describe mathematically and biological evolution of RNA viruses, theoretical models of virus evolution have been proposed, and many of their predictions were experimentally confirmed. This study aimed to simulate and analyze computationally a model of viral evolution that represents evolutionary relationships between the population of viral RNA genome and the different selective pressures on it in its interaction with the host organism. It also aimed to develop computational simulation software for the viral evolution model, and demonstrate the possibility of describing the model as a Galton-Watson branching process. Among the results and discussions outlined, there are an analytical criterion to study the recovery time and the critical regime of a Galton-Watson branching process applied to viral evolution; predictions about the correlation between factors of the host organism and the evolutionary dynamics of viral population; predictions about the contribution of mutational rate, the size and maximum replicative capacity of viral population for the prognosis and four stages of infection: recovery time, mutation-selection equilibrium, extinction threshold, and lethal mutagenesis.