Evolutionary Dynamics of Infection

Abstract

Mathematical models of infection represent a robust and reproducible framework, which can be used to understand complex phenomena in different areas of biology or the social sciences, as well as draw insightful analogies between them. This thesis presents the development and application of mathematical techniques to study infection at different scales, and in different organisms, from antiviral drug resistance of human viruses, to pathogen avoidance in C. elegans, to multiplicity reactivation in viruses, to excessive force behavior of police officers. First, we use a model of infection dynamics within a host organism to propose and study "cryptic resistance", a mechanism that could allow viruses to build resistance to antiviral drug therapies by synchronizing their life cycle with the pattern of the therapy. Second, we propose the nematode Caenorhabditis elegans as a model organism for quantitative biological studies in general and for the study of infection response in particular. We present a phenomenological model of how C. elegans collectively avoids pathogenic bacteria as a response to infection. Third, we explore mechanisms similar to multiplicity reactivation in viruses, mechanisms that would have been necessary to generate the first self-replicating cells, a critical step in the origin of life. Finally, we propose a mathematical framework to test whether violent behavior and excessive force are contagious among civilians and police officers.