A viral load-based cellular automata approach to modeling HIV dynamics and drug treatment.

We formulated a novel cellular automata (CA) model for HIV dynamics and drug treatment. The model is built upon realistic biological processes, including the virus replication cycle and mechanisms of drug therapy. Viral load, its effect on infection rate, and the role of latently infected cells in sustaining HIV infection are among the aspects that are explored and incorporated in the model. We assume that the calculation of the number of cells in the neighborhood which influences the center cell's state is based on the viral load. This variable-cell neighborhood enables the simulation of an infection rate that is correlated to the viral load. This approach leads to a new and flexible way of modeling HIV dynamics and allows for the simulation of different antiretroviral drug treatments based on their individual and combined effects. The results of the simulation show the three phases of HIV dynamics (acute, chronic, and AIDS) and the additional drug response phase when drug treatment is added. The dynamics from the model qualitatively match clinical data. Drug treatment combinations with reverse transcriptase inhibitors and protease inhibitors are simulated using various drug efficacies. The results indicate that the model can be very useful in evaluating different drug therapy regimens.