ArticleProceedings of the National Academy of Sciences of the United States of America2018
Probabilistic control of HIV latency and transactivation by the Tat gene circuit.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- AZD5582 robustly reactivates latently infected cells and clears the majority of those reactivated from the SIV reservoir.bioRxiv : the preprint server for biology · 2026Article
- Neuro-Dynamic Quantitative Systems Pharmacology (QSP) model describing Alzheimer's disease pathophysiology and treatment effects.NPJ systems biology and applications · 2026Article
- Progenitor neighborhoods function as transient niches to sustain olfactory neurogenesis.Stem cell reports · 2025Article
- Mathematical modeling and mechanisms of HIV latency for personalized anti latency therapies.NPJ systems biology and applications · 2025Review
- Modelling HIV-1 control and remission.NPJ systems biology and applications · 2024Review
- Review
- Review
- Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.Frontiers in cell and developmental biology · 2023Article
- Network design principle for robust oscillatory behaviors with respect to biological noise.eLife · 2022Article
- A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription.PLoS computational biology · 2022Article
- A Higher-Order Galerkin Time Discretization and Numerical Comparisons for Two Models of HIV Infection.Computational and mathematical methods in medicine · 2022Article
- Unified model of short- and long-term HIV viral rebound for clinical trial planning.Journal of the Royal Society, Interface · 2021Article
- Exact Probability Landscapes of Stochastic Phenotype Switching in Feed-Forward Loops: Phase Diagrams of Multimodality.Frontiers in genetics · 2021Article
- Review
- Advances toward Curing HIV-1 Infection in Tissue Reservoirs.Journal of virology · 2020Review
- Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers.PLoS computational biology · 2019Article
- Microglial Cells: The Main HIV-1 Reservoir in the Brain.Frontiers in cellular and infection microbiology · 2019Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
The reservoir of HIV latently infected cells is the major obstacle for eradication of HIV infection. The "shock-and-kill" strategy proposed earlier aims to reduce the reservoir by activating cells out of latency. While the intracellular HIV Tat gene circuit is known to play important roles in controlling latency and its transactivation in HIV-infected cells, the detailed control mechanisms are not well understood. Here we study the mechanism of probabilistic control of the latent and the transactivated cell phenotypes of HIV-infected cells. We reconstructed the probability landscape, which is the probability distribution of the Tat gene circuit states, by directly computing the exact solution of the underlying chemical master equation. Results show that the Tat circuit exhibits a clear bimodal probability landscape (i.e., there are two distinct probability peaks, one associated with the latent cell phenotype and the other with the transactivated cell phenotype). We explore potential modifications to reactions in the Tat gene circuit for more effective transactivation of latent cells (i.e., the shock-and-kill strategy). Our results suggest that enhancing Tat acetylation can dramatically increase Tat and viral production, while increasing the Tat-transactivation response binding affinity can transactivate latent cells more rapidly than other manipulations. Our results further explored the "block and lock" strategy toward a functional cure for HIV. Overall, our study demonstrates a general approach toward discovery of effective therapeutic strategies and druggable targets by examining control mechanisms of cell phenotype switching via exactly computed probability landscapes of reaction networks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.