| Abstrakt: |
One of the challenges in creating an effective vaccine for HIV-1 is that the virus mutates and consequently escapes detection by the immune response. Understanding the conditions under which a mutant viral strain establishes dominance in the virus population is crucial to developing effective therapies. Mutant viral escape in HIV-1 infection is believed to be due largely to pressure from the cytotoxic T lymphocyte (CTL) response. Published data report that CTL escape mutants can arise at what seem to be unpredictable times during infection. In this work, we present an agent-based stochastic computational model of HIV-1 viral escape, with the aim of determining conditions on CTL efficacies that are likely to lead to mutant escape. Our model algorithm reproduces the steps of infection and the immune response: viral infection of target cells, viral mutation, CTL killing, and viral production. We include parameters for viral burst size, mutation rate, and the probabilities of recognition and elimination by CTLs. Our model is able to reproduce the CTL escape phenomena seen in clinical data, with the emergence of escape mutants occurring at various times in either acute or chronic infection. Moreover, in both cases, the time to complete escape can range from very rapid to very slow. With this model we are able to explore simulated escape scenarios over large populations and over long time periods. We present results from simulations of viral production by both wild type and mutant strains in 1000 in silico individuals over 20 years. Because the model so closely reflects the biological processes involved in viral infection and CTL killing, it can be used to simulate any scenario in which a virus mutates in response to CTL pressure. Such results can aid in the development of effective vaccines. [ABSTRACT FROM AUTHOR] |
