Abstract
Computational models can facilitate the understanding of complex biomedical systems such as in HIV/AIDS. Untangling the dynamics between HIV and CD4+ cellular populations and molecular interactions can be used to investigate the effective points of interventions in the HIV life cycle. With that in mind, we have developed a state transition systems dynamics and stochastic model that can be used to examine various alternatives for the control and treatment of HIV/AIDS. The specific objectives of our study were to use a cellular/molecular model to study optimal chemotherapies for reducing the HIV viral load and to use the model to study the pattern of mutant viral populations and resistance to drug therapies. The model considers major state variables (uninfected CD4+ lymphocytes, infected CD4+ cells, replicated virions) along with their respective state transition rates (viz. CD4+ replacement rate, infection rate, replication rate, depletion rate). The state transitions are represented by ordinary differential equations. The systems dynamics model was used for a variety of computational experimentations to evaluate HIV mutations, and to evaluate effective strategies in HIV drug therapy interventions.
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