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Stochastic Models of Lymphocyte Proliferation and Death

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Journal PLoS One
Date 2010 Oct 14
PMID 20941358
Citations 32
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Abstract

Quantitative understanding of the kinetics of lymphocyte proliferation and death upon activation with an antigen is crucial for elucidating factors determining the magnitude, duration and efficiency of the immune response. Recent advances in quantitative experimental techniques, in particular intracellular labeling and multi-channel flow cytometry, allow one to measure the population structure of proliferating and dying lymphocytes for several generations with high precision. These new experimental techniques require novel quantitative methods of analysis. We review several recent mathematical approaches used to describe and analyze cell proliferation data. Using a rigorous mathematical framework, we show that two commonly used models that are based on the theories of age-structured cell populations and of branching processes, are mathematically identical. We provide several simple analytical solutions for a model in which the distribution of inter-division times follows a gamma distribution and show that this model can fit both simulated and experimental data. We also show that the estimates of some critical kinetic parameters, such as the average inter-division time, obtained by fitting models to data may depend on the assumed distribution of inter-division times, highlighting the challenges in quantitative understanding of cell kinetics.

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References
1.
Callard R, Hodgkin P . Modeling T- and B-cell growth and differentiation. Immunol Rev. 2007; 216:119-29. DOI: 10.1111/j.1600-065X.2006.00498.x. View

2.
Revy P, Sospedra M, Barbour B, Trautmann A . Functional antigen-independent synapses formed between T cells and dendritic cells. Nat Immunol. 2001; 2(10):925-31. DOI: 10.1038/ni713. View

3.
Lyons A . Analysing cell division in vivo and in vitro using flow cytometric measurement of CFSE dye dilution. J Immunol Methods. 2000; 243(1-2):147-54. DOI: 10.1016/s0022-1759(00)00231-3. View

4.
de Boer R, Oprea M, Antia R, Ahmed R, Perelson A . Recruitment times, proliferation, and apoptosis rates during the CD8(+) T-cell response to lymphocytic choriomeningitis virus. J Virol. 2001; 75(22):10663-9. PMC: 114648. DOI: 10.1128/JVI.75.22.10663-10669.2001. View

5.
Deenick E, Gett A, Hodgkin P . Stochastic model of T cell proliferation: a calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival. J Immunol. 2003; 170(10):4963-72. DOI: 10.4049/jimmunol.170.10.4963. View