Mutation, Fitness, Viral Diversity, and Predictive Markers of Disease Progression in a Computational Model of HIV Type 1 Infection

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Abstract
The aim of this study was to develop a computational model of HIV infection able to simulate the natural history of the disease and to test predictive parameters of disease progression. We describe the results of a numerical simulation of the cellular and humoral immune response to HIV-1 infection as an adaptive pathway in a "bit-string" space. A total of 650 simulations of the HIV-1 dynamics were performed with a modified version of the Celada–Seiden immune system model. Statistics are in agreement with epidemiological studies showing a log normal distribution for the time span between infection and the development of AIDS. As predictive parameters of disease progression we found that HIV-1 accumulates "bit" mutations mainly in the peptide sequences recognized by cytotoxic CD8 T cells, indicating that cell-mediated immunity plays a major role in viral control. The viral load set point was closely correlated with the time from infection to development of AIDS. Viral divergence from the viral quasispecies that was present at the beginning of infection in long-term nonprogressors (LTNP) was found to be similar to that found in rapid progressors at the time CD4 T cells drop below the critical value of 200 cells/µl. In contrast, the diversity indicated by the number of HIV strains present at the same time was higher for rapid and normal progressors compared to LTNP, suggesting that the early immune response can make the difference. This computational model may help to define the predictive parameters of HIV dynamics and disease progression, with potential applications in therapeutic and vaccine simulations.