Dynamics and Evolution of Virus and Immune Response Networks

University Of Louisiana At Lafayette, Lafayette LA

Investigators

Abstract

This research develops new mathematical and computational methods to understand the competition between evolving viruses (like HIV) and the immune system, which seeks to eradicate them. The dynamics of virus and immune response within an infected host can be viewed as a complex ecosystem. During HIV infection, an extensive repertoire of immune cells target the virus, while HIV can rapidly evolve resistance to multiple immune responses. The ensuing battle precipitates a dynamic network of interacting viral strains and immune response variants. Understanding the main factors shaping viral resistance pathways and immune dynamics is crucial for designing effective vaccines and immunotherapies. Mathematical modeling can help elucidate patterns of viral escape from immune attack, however overall system complexity challenges the analysis of such models. This project develops mathematical models and analytical techniques for understanding the dynamics of multiple virus and immune response populations within infected hosts. This research uses dynamical systems and computational methods to characterize persistent structures of the evolving virus-immune response network. Potential HIV vaccine and treatment strategies will be investigated utilizing the mathematical models. Furthermore, the models will be connected to Simian Immunodeficiency Virus (SIV) data in collaboration with biologists. Also significant are the educational impacts arising from the interdisciplinary training of graduate and undergraduate students in this research project. Rapidly evolving pathogens, such as SIV and HIV, can evade the host immune responses via mutations at several targeted epitopes (viral proteins). The concurrent interaction of diverse virus and immune populations necessitate considering a complex system in order to understand viral escape at multiple epitopes. This project develops and analyzes mathematical models for virus and immune response variants interacting in a network. Stability and persistence of viral/immune populations will be characterized in terms of threshold quantities and the structure of the underlying interaction network. An important question is to determine what mechanisms cause the virus to win this battle in most drug-naive HIV infected patients, and alternatively what leads to long-term viral control in a small percentage of drug-naive HIV infected individuals known as Elite Controllers. Results from the differential equation models and stochastic counterparts will inform upon viral escape of immune response at multiple epitopes, along with the more complex scenario of virus-antibody coevolution. From a broader perspective, the methods will be applicable to other predator-prey ecological networks, for example phage-bacteria communities. Extensions relevant to HIV biology, such as incorporating heterogeneous immune recognition kinetics during the infected cell lifecycle, latent cell infection and different vaccine/immunotherapy strategies, will be investigated. Results will be linked to data, in particular through interdisciplinary collaboration connecting the models with viral genomic and immune cell data from SIV infection experiments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.