The most significant obstacle in eradicating HIV from patients is eliminating transcriptionally quiescent or latently infected cells. To overcome this obstacle, HIV researchers are working toward a `shock and kill' strategy that will reactivate the reservoir of latent cells so that they can be eliminated by apoptosis and antiretroviral therapy. Transcriptional activators have been insufficient in fully reactivating latency, and their non-specificity causes widespread induction of host-cell pathways leading to cell activation, inflammation, and cell death. This drives the need for discovering novel compounds and combination drug cocktails that are both specific and potent to HIV. Positive feedback by HIV's master regulator, Tat, is required for latent reactivation and feedback modulators are currently under-represented. Previous measurements of feedback strength demonstrated that modulation of feedback directly affects HIV latency (Weinberger, Dar, Simpson, Nat Gen 2008). Feedback strength is measured by analyzing the frequency component of fluctuations in HIV gene expression, (i.e. `noise'). Developing a drug screen for feedback modulators which utilizes shifts in the frequency domain of viral gene expression as an orthogonal measure to current methodologies will enable the detection of compounds that are specific to Tat and the >30 co-factors and human proteins involved in the feedback loop, thus limiting unwanted off-target effects. Addition of feedback modulators to latency cocktails will enable modulation of transcription and feedback separately or in unison for maximal control of biasing the latent fate decision. Increased regulatory control will provide a new class of feedback drug cocktails for full elimination or indefinite inactivation of the latent reservoir. To pursue this screening approach, we propose to use noise analysis to decouple transcriptional feedback dynamics from episodic transcription caused by the viral promoter. In Aim 1, we will develop a high- throughput screening pipeline that analyzes HIV feedback strength using time-lapse single-cell imaging and analysis of noise in gene expression. ~20 well-known compounds that target HIV transcription and/or feedback will initially be used to calibrate and optimize the dynamic range of the pipeline for detecting compounds. We will then characterize >60 compounds that we previously identified to synergize reactivation of latency (Dar et al., Science 2014) and quantify their modulation of transcription and/or feedback strength as a test case for the system with compounds transcriptionally relevant to HIV. In Aim 2, we will use this imaging and noise analysis pipeline to conduct a large-scale drug screen and identify novel feedback modulators among ~10,000 compounds. Finally, we will assess drug cocktails that contain modulators of both feedback and/or transcription to maximize full reactivation or inactivation of latency in primary cell models. Leading cocktails will later be tested on patient samples. We anticipate that through our research, we will identify feedback compounds and drug cocktails that allow increased control for biasing HIV latency. We also expect that detection of feedback modulators and frequency shifts in gene expression will represent a new orthogonal axis to drug discovery for compounds that cannot be detected in current drug screens. Finally, this method may extend to a variety of disease models and additional auto-regulated systems.