Key Points
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Modelling the effects of antiretroviral therapy on human immunodeficiency virus (HIV) infection has shown that HIV replicates rapidly and is cleared rapidly in chronically infected individuals. Four distinct timescales have been identified: virion clearance, hours; death of productively infected CD4+ T cells, days; death of long-lived infected cells, weeks; loss of latently infected cells, months to years; and loss of HIV from the FDC network, months to years.
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Models of virus infection have been applied to the chronic stages of hepatitis C virus (HCV), hepatitis B virus (HBV) and cytomegalovirus (CMV) infection, and show that all three infections are characterized by rapid dynamics.
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The efficacy and mode of action of antiviral drugs can sometimes be shown from viral-dynamic studies; the relative efficacy of different drug doses and treatment regimes can be estimated from viral-decay curves; and the rapid decay of HCV RNA suggests that interferon blocks HCV production in a dose-dependent manner.
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Modelling antiviral immune responses is still in its infancy, but it raises interesting quantitative questions about both cytopathic and non-cytopathic effects of CD8 T+ cells in controlling viral infections.
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The dynamics of lymphocyte populations in HIV-infected and -uninfected individuals have been elucidated through labelling studies combined with models.
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Future quantitative understanding of the immune system might be speeded by a combination of experiment, formation of immunological databases, modelling and computer simulation.
Abstract
During the past 6 years, there have been substantial advances in our understanding of human immunodeficiency virus 1 and other viruses, such as hepatitis B virus and hepatitis C virus, that cause chronic infection. The use of mathematical modelling to interpret experimental results has made a significant contribution to this field. Mathematical modelling is also improving our understanding of T-cell dynamics and the quantitative events that underlie the immune response to pathogens.
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Acknowledgements
This work was supported by the US Department of Energy and the National Institutes of Health. I thank R. Ribeiro for helpful comments.
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Glossary
- CD4 COUNT
-
A normal CD4 count is 1,000 per μl, with a range of 600–1,400 per μl. The count falls during primary infection, then returns to near or lower than normal levels. It then slowly falls, taking many years to reach the level of 200 per μl that characterizes AIDS.
- V
-
Various notations have been used in modelling viral infections. V, the concentration of free virus, in the case of HIV, is measured in units of HIV-1 RNA per ml. As there are two RNAs per virion, the true concentration of virions is half the RNA concentration.
- I
-
Productively infected cells have here been denoted I. In the HIV literature, as there are different types of infected cells, T*, has been used instead of I to denote the population of productively infected CD4+ T cells. In the case of HCV and HBV, I denotes infected hepatocytes.
- NONLINEAR LEAST SQUARES
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A procedure that estimates the parameters in a model by minimizing the differences between model predictions and data.
- FOLLICULAR DENDRITIC CELLS
-
(FDCs). Specialized non-haematopoietic stromal cells that reside in lymphoid follicles and germinal centres. These cells have long dendrites and carry intact antigen on their surface for long periods of time.
- INTERFERON-α
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(IFN-α). A cytokine secreted by many cell types in an early response to viral infection. IFN-α has pleiotropic antiviral effects, including inhibition of protein synthesis and DNA replication, enhanced antigen presentation and natural killer-cell activation.
- HCV GENOTYPE
-
The nucleotide sequence of HCV is highly variable, with the most divergent isolates sharing only 60% nucleotide sequence homology. On the basis of sequence similarity, isolates have been grouped into six main types, called genotypes.
- MHC TETRAMERS
-
Reagents composed of four MHC–peptide complexes linked by biotinylation, which can be fluorescently labelled and used to track antigen-specific T cells by flow cytometry.
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Perelson, A. Modelling viral and immune system dynamics. Nat Rev Immunol 2, 28–36 (2002). https://doi.org/10.1038/nri700
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DOI: https://doi.org/10.1038/nri700
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