The primary barrier to eliminating HIV with antiretroviral therapy (ART) is its persistence as a long-lived transcriptionally silent “latent” population. Several host and epigenetic factors affect viral latency and its long-term stability. Underlying these, however, is HIV’s genetic circuitry that drives viral decision-making: large stochastic gene-expression fluctuations driven by HIV’s LTR promoter, together with Tat-mediated positive feedback, execute the latent-or-active decision. However, it remains unknown how altering these fluctuations affects the establishment of latency upon infection. This fluctuation-driven decision-making also explains to a large extent why potent transcriptional activators always reactivate only a fraction of latent HIV. Again, how these gene-expression fluctuations affect reactivation is unknown. The objective of this proposal is to quantify how increasing or suppressing HIV’s gene-expression fluctuations alters viral latency establishment and reactivation. The study design relies on HIV-based circuits engineered to alter gene-expression fluctuations and single-cell time-lapse microscopy to quantify the magnitude of fluctuations. First, we utilize the natural sequence diversity of HIV strains to engineer LTR-variant circuits that directly test whether increasing or decreasing gene-expression fluctuations affects latency establishment. Results from these circuits will be directly contrasted with the level of gene-expression fluctuations measured from patient-derived proviral latent clones (obtained from Dr. R. Siliciano). Together, this will reveal whether the clinical latent reservoir is enriched or suppressed for gene-expression fluctuations. Second, we use a full-length dual-reporter HIV construct to isolate latent cells (thousands of diverse integration sites) and quantify how gene-expression fluctuations change their likelihood for reactivation in the presence of compounds (such as known transcriptional activators). Overall, the expected outcome of this work will be a quantitative model capable of predicting how altering LTR gene-expression fluctuations alters latent reactivation in patients under ART. This quantification will impact the development of combination therapies to achieve maximal reactivation of the latent reservoir.