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Current Opinion in HIV and AIDS
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Current Opinion in HIV and AIDS
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Dynamic models of viral replication and latency

Authors: Mohammadi Pejman; Ciuffi Angela; Beerenwinkel Niko;

Dynamic models of viral replication and latency

Abstract

Purpose of review HIV targets primary CD4+ T cells. The virus depends on the physiological state of its target cells for efficient replication, and, in turn, viral infection perturbs the cellular state significantly. Identifying the virus–host interactions that drive these dynamic changes is important for a better understanding of viral pathogenesis and persistence. The present review focuses on experimental and computational approaches to study the dynamics of viral replication and latency. Recent findings It was recently shown that only a fraction of the inducible latently infected reservoirs are successfully induced upon stimulation in ex-vivo models while additional rounds of stimulation make allowance for reactivation of more latently infected cells. This highlights the potential role of treatment duration and timing as important factors for successful reactivation of latently infected cells. The dynamics of HIV productive infection and latency have been investigated using transcriptome and proteome data. The cellular activation state has shown to be a major determinant of viral reactivation success. Mathematical models of latency have been used to explore the dynamics of the latent viral reservoir decay. Summary Timing is an important component of biological interactions. Temporal analyses covering aspects of viral life cycle are essential for gathering a comprehensive picture of HIV interaction with the host cell and untangling the complexity of latency. Understanding the dynamic changes tipping the balance between success and failure of HIV particle production might be key to eradicate the viral reservoir.

Current Opinion in HIV and AIDS, 10 (2)

ISSN:1746-630X

ISSN:1746-6318

Country
Switzerland
Keywords

HIV, HIV Infections, Transcriptome dynamics, Virus Replication, Cure; Dynamic model; HIV; Latency; Transcriptome dynamics, Models, Biological, Dynamic model, Virus Latency, Host-Pathogen Interactions, Latency, HIV-1, Humans, GENOMICS IN HIV INFECTION: Edited by Amalio Telenti, Transcriptome, Cure

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Average
Top 10%
Green
hybrid