Wei, X. et al. Viral dynamics in human immunodeficiency virus type I infection. Nature 373, 117−122
ABSTRACT The dynamics of HIV-1 replication in vivo are largely unknown yet they are critical to our understanding of disease pathogenesis. Experimental drugs that are potent inhibitors of viral replication can be used to show that the composite lifespan of plasma virus and virus-producing cells is remarkably short (half-life approximately 2 days). Almost complete replacement of wild-type virus in plasma by drug-resistant variants occurs after fourteen days, indicating that HIV-1 viraemia is sustained primarily by a dynamic process involving continuous rounds of de novo virus infection and replication and rapid cell turnover.
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- "The infection results in high T-cell activation and turnover. An immediately intuitive assumption is that HIV-mediated destruction of CD4 + cells directly reduces the number of these cells and that the high turnover rates of T cells and the slow progression to AIDS reflect a long but eventually lost struggle of the immune system to replace killed cells in its effort to maintain T-cell homeostasis    . However, HIV mainly infects activated CD4 + cells, and activated cells normally follow different dynamics than cells that belong to resting populations whose numbers are controlled by homeostatic mechanisms. "
ABSTRACT: An analysis of the model underpinning the description of the spread of HIV infection of CD4 + T cells is examined in detail in this work. Investigations of the disease free and endemic equilibrium are done using the method of Jacobian matrix. An iteration technique, namely, the homotopy decomposition method (HDM), is implemented to give an approximate solution of nonlinear ordinary differential equation systems. The technique is described and illustrated with numerical examples. The approximated solution obtained via HDM is compared with those obtained via other methods to prove the trustworthiness of HDM. Moreover, the lessening and simplicity in calculations furnish HDM with a broader applicability.BioMed Research International 06/2014; 2014(2014):10. DOI:10.1155/2014/618404 · 2.71 Impact Factor
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- "The genetic diversity of HIV within a patient is large as HIV is prone to errors during replication (Overbaugh and Bangham, 2001; Rambaut et al., 2004). Furthermore, the virus population has a high turnover with a mean half-life of 1–2 days (Coffin, 1995; Ho et al., 1995; Perelson et al., 1996; Wei et al., 1995). The high heritability , the fitness differences between virus genotypes, the large virus population size within a host, the high mutation rate and the short generation time together lead to the expectation that within-host evolution should lead to higher viral loads over the long duration of the asymptomatic phase (Read and Taylor, 2001). "
ABSTRACT: The asymptomatic phase of HIV-1 infections is characterised by a stable set-point viral load (SPVL) within patients. The SPVL is a strong predictor of disease progression and shows considerable variation of multiple orders of magnitude between patients. Recent studies have found that the SPVL in donor and recipient pairs is strongly correlated indicating that the virus genotype strongly influences viral load. Viral genetic factors that increase both viral load and the replicative capacity of the virus would result in rapid within-host evolution to higher viral loads. Reconciling a stable SPVL over time with high SPVL heritability requires viral genetic factors that strongly influence SPVL but only weakly influence the competitive ability of the virus within hosts. We propose a virus trait that affects the activation of target cells, and therefore viral load, but does not confer a competitive advantage to the virus. We incorporate this virus-induced target cell activation into within- and between-host models and determine its effect on the competitive ability of virus strains and on the variation in SPVL in the host population. On the within-host level, our results show that higher rates of virus-induced target cell activation increase the SPVL and confer no selective advantage to the virus. This leads to a build up of diversity in target cell activation rates in the virus population during within-host evolution. On the between-host level, higher rates of target cell activation and therefore higher SPVL affect the transmission potential of the virus. Random selection of a new founder strain from the diverse virus population within a donor results in a standing variation in SPVL in the host population. Therefore, virus-induced target cell activation can explain the heritability of SPVL, the absence of evolution to higher viral loads during infection and a large standing variation in SPVL between hosts.12/2013; 5(4):174-180. DOI:10.1016/j.epidem.2013.09.002
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- "Interestingly, these results were based on mathematical analyses of clinical data. Estimating the decline in viral load of patients following the initiation of antiviral therapy (or plasma removal by apheresis technique; Ramratnam et al., 1999) shows us that HIV is cleared from patients at a rapid rate, with a half-life of around 6 h (Ho et al., 1995; Wei et al., 1995; Perelson et al., 1996). This estimation of rapid virus turnover implies that HIV resistance to any single drug could quickly emerge, highlighting the importance of combination therapies as they reduce the chances of drug resistance developing (Coffin, 1995; Ho et al., 1995; Wei et al., 1995; Perelson et al., 1996; Perelson and Nelson, 1999). "
ABSTRACT: Analyzing the time-course of several viral infections using mathematical models based on experimental data can provide important quantitative insights regarding infection dynamics. Over the past decade, the importance and significance of mathematical modeling has been gaining recognition among virologists. In the near future, many animal models of human-specific infections and experimental data from high-throughput techniques will become available. This will provide us with the opportunity to develop new quantitative approaches, combining experimental and mathematical analyses. In this paper, we review the various quantitative analyses of viral infections and discuss their possible applications.Frontiers in Microbiology 09/2013; 4:264. DOI:10.3389/fmicb.2013.00264 · 3.99 Impact Factor