Review. An introduction to lymphocyte and viral dynamics: the power and limitations of mathematical analysis

Asquith, Becca; Bangham, Charles R. M.

doi:10.1098/rspb.2003.2386

Cited by 33 publications

(19 citation statements)

References 31 publications

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“…Mathematical approaches to studying host-pathogen interactions have increased steadily in the last four decades. (For entry into the corresponding body of literature, we recommend [27], [28], [29], [30], [31]). Mathematical analysis can be rigorous (i.e., comprehensive, thorough and exact) and a good model should be able to withstand such analysis.…”

Section: Introductionmentioning

confidence: 99%

The Cycle of EBV Infection Explains Persistence, the Sizes of the Infected Cell Populations and Which Come under CTL Regulation

et al. 2013

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Previous analysis of Epstein-Barr virus (EBV) persistent infection has involved biological and immunological studies to identify and quantify infected cell populations and the immune response to them. This led to a biological model whereby EBV infects and activates naive B-cells, which then transit through the germinal center to become resting memory B-cells where the virus resides quiescently. Occasionally the virus reactivates from these memory cells to produce infectious virions. Some of this virus infects new naive B-cells, completing a cycle of infection. What has been lacking is an understanding of the dynamic interactions between these components and how their regulation by the immune response produces the observed pattern of viral persistence. We have recently provided a mathematical analysis of a pathogen which, like EBV, has a cycle of infected stages. In this paper we have developed biologically credible values for all of the parameters governing this model and show that with these values, it successfully recapitulates persistent EBV infection with remarkable accuracy. This includes correctly predicting the observed patterns of cytotoxic T-cell regulation (which and by how much each infected population is regulated by the immune response) and the size of the infected germinal center and memory populations. Furthermore, we find that viral quiescence in the memory compartment dictates the pattern of regulation but is not required for persistence; it is the cycle of infection that explains persistence and provides the stability that allows EBV to persist at extremely low levels. This shifts the focus away from a single infected stage, the memory B-cell, to the whole cycle of infection. We conclude that the mathematical description of the biological model of EBV persistence provides a sound basis for quantitative analysis of viral persistence and provides testable predictions about the nature of EBV-associated diseases and how to curb or prevent them.

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Section: Introductionmentioning

confidence: 99%

The Cycle of EBV Infection Explains Persistence, the Sizes of the Infected Cell Populations and Which Come under CTL Regulation

et al. 2013

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“…The problem of estimating the turnover rate of immune cells is not new and has been approached with the use of BrdU and d-glucose labels allowing to distinguish between divided and undivided cells (for a review see Asquith and Bangham, 2003;De Boer et al, 2003). In the past few years, BrdU assay has been supplemented by the CarboxyFluorescein diacetate Succinimidyl Ester (CFSE) label which provides a more detailed information on the proliferation history of labelled cells (Lyons and Parish, 1994).…”

Section: Introductionmentioning

confidence: 99%

Quantifying cell turnover using CFSE data

Ganusov

Pilyugin

Boer

et al. 2005

Journal of Immunological Methods

120

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The CFSE dye dilution assay is widely used to determine the number of divisions a given CFSE labelled cell has undergone in vitro and in vivo. In this paper, we consider how the data obtained with the use of CFSE (CFSE data) can be used to estimate the parameters determining cell division and death. For a homogeneous cell population (i.e., a population with the parameters for cell division and death being independent of time and the number of divisions cells have undergone), we consider a specific biologically based bSmith-MartinQ model of cell turnover and analyze three different techniques for estimation of its parameters: direct fitting, indirect fitting and rescaling method. We find that using only CFSE data, the duration of the division phase (i.e., approximately the S + G 2 + M phase of the cell cycle) can be estimated with the use of either technique. In some cases, the average division or cell cycle time can be estimated using the direct fitting of the model solution to the data or by using the Gett-Hodgkin method [Gett A. and Hodgkin, P. 2000. A cellular calculus for signal integration by T cells. Nat. Immunol. 1:239-244]. Estimation of the death rates during commitment to division (i.e., approximately the G 1 phase of the cell cycle) and during the division phase may not be feasible with the use of only CFSE data. We propose that measuring an additional parameter, the fraction of cells in division, may allow estimation of all model parameters including the death rates during different stages of the cell cycle. D

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“…For more than a decade, mechanism-based mathematical approaches have proved useful for understanding the viral dynamics of immune responses (Perelson and Weisbuch, 1997;Asquith and Bangham, 2003). Much of the research has focused on chronic infectious diseases such as human immunodeficiency virus (HIV), human T cell lymphotropic virus (HTLV), and hepatitis (Perelson, 2002;Wodarz, 2006;Asquith and Bangham, 2007).…”

Section: Toxicodynamics Of Skin Allergymentioning

confidence: 99%

From pathways to people: applying the adverse outcome pathway (AOP) for skin sensitization to risk assessment

MacKay

Davies

Summerfield

et al. 2013

ALTEX

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Review. An introduction to lymphocyte and viral dynamics: the power and limitations of mathematical analysis

Cited by 33 publications

References 31 publications

The Cycle of EBV Infection Explains Persistence, the Sizes of the Infected Cell Populations and Which Come under CTL Regulation

The Cycle of EBV Infection Explains Persistence, the Sizes of the Infected Cell Populations and Which Come under CTL Regulation

Quantifying cell turnover using CFSE data

From pathways to people: applying the adverse outcome pathway (AOP) for skin sensitization to risk assessment

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