A computational model of inhibition of HIV-1 by interferon-alpha

Type 1 interferons such as interferon-alpha (IFN α ) inhibit replication of Human immunodeficiency virus (HIV-1) by upregulating the expression of genes that interfere with specific steps in the viral life cycle. This pathway thus represents a potential target for immune-based therapies that can alter the dynamics of host-virus interactions to benefit the host. To obtain a deeper mechanistic understanding of how IFNα impacts spreading HIV-1 infection, we modeled the interaction of HIV-1 with CD4 T cells and IFN α as a dynamical system. This model was then tested using experimental data from a cell culture model of spreading HIV-1 infection. We found that a model in which IFN α induces reversible cellular states that block both early and late stages of HIV-1 infection, combined with a saturating rate of conversion to these states, was able to successfully fit the experimental dataset. Sensitivity analysis showed that the potency of inhibition by IFN α was particularly dependent on specific network parameters and rate constants. This model will be useful for designing new therapies targeting the IFN α network in HIV-1-infected individuals, as well as potentially serving as a template for understanding the interaction of IFN α with other viruses. Author Summary Interferon-alpha (IFN α ) is a key component of the host response to HIV-1, but the details of how IFN α regulates infection are still incompletely understood. To provide a deeper understanding of the dynamics of how IFN α inhibits HIV-1, we simulated the interaction of IFN α and HIV-1 as a computational model and compared this model to an experimental dataset. We identify a model structure that is able to fit many key features of the data. Furthermore, we use the model to predict optimal strategies for targeting the IFN α pathway therapeutically. We anticipate that this model will be useful for further analysis of HIV-IFNα interactions and will help to guide new therapeutic strategies.