Computational modeling of populations of RNA virus and the evolution of their phenotypic diversity

• Grant number: 14/13382-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: October 01, 2014
• End date: September 30, 2016
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Fernando Martins Antoneli Junior
• Grantee: Luiza Guimarães Fabreti
• Host Institution: Escola Paulista de Medicina (EPM). Universidade Federal de São Paulo (UNIFESP). Campus São Paulo. São Paulo , SP, Brazil

Abstract

The project aims to answer the following question: "How the phenotypic diversity of a population of RNA viruses depends on the probabilities of occurrence of deleterious, beneficial and neutral effects ?" Viruses with RNA genomes exhibit a high degree of genotypic and phenotypic diversity, which in turn plays a fundamental role in the mechanisms responsible for conferring selective advantages when a viral population is trying to establish itself in a host. Indeed, several experimental studies show that generating diversity is advantageous when the virus invade a new host and faces different selective pressures imposed by a hostile environment. These pressures can be overcome through evolutionary escape generated by diversification. Therefore, to understand and model the dynamics of these escape processes is of direct relevance to clinical decision making and public health. The behavior of phenotypic diversity is obtained numerically through computer simulation, also called in silico experimentation. The computer simulation is performed with the Virusim program, which implements a stochastic model for viral replication based on the theory of quasi-species and the theory of branching processes. In order to obtain the phenotypic diversity à as a function of the deleterious probability p and the beneficial probability q, we will perform simulations of the evolution of the population keeping the maximum replicative capacity R constant and varying the beneficial probability q in a range of values that contains the numbers measured in the laboratory.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FABRETI, LUIZA GUIMARAES; CASTRO, DIOGO; GORZONI, BRUNO; RAMOS JANINI, LUIZ MARIO; ANTONELI, FERNANDO. Stochastic Modeling and Simulation of Viral Evolution. Bulletin of Mathematical Biology, v. 81, n. 4, p. 1031-1069, APR 2019. (14/13382-1)