Literature DB >> 11926122

Modeling deuterated glucose labeling of T-lymphocytes.

Ruy M Ribeiro1, Hiroshi Mohri, David D Ho, Alan S Perelson.   

Abstract

Human immunodeficiency virus type 1 (HIV-1) infects cells of the immune system and leads to depletion of CD4+ T cells, and to an increase of CD8+ T-lymphocytes. However, not much is known about the dynamics of turnover (proliferation and death) of the CD4+ and CD8+ T cell populations in HIV-infected and healthy individuals. A new experimental technique has been developed using deuterated-glucose labeling that provides information on cell turnover in vivo. However, the quantitative interpretation of the data requires the development of specific dynamic models. In this paper we derive two models, a simple one-compartment model and a more complex two-compartment model. These models allow for robust quantification of death and proliferation rates, but careful consideration of the system is necessary to understand what is being measured in each case. We demonstrate that more realistic models can account not only for differences in the turnover rates between HIV-infected and healthy individuals, but also take into consideration the elevated state of activation in HIV infection. The use of these models in the interpretation of the experimental data will increase our knowledge of T cell dynamics in the context of HIV infection.

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Year:  2002        PMID: 11926122     DOI: 10.1006/bulm.2001.0282

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  12 in total

1.  Modelling deuterium labelling of lymphocytes with temporal and/or kinetic heterogeneity.

Authors:  Rob J De Boer; Alan S Perelson; Ruy M Ribeiro
Journal:  J R Soc Interface       Date:  2012-04-18       Impact factor: 4.118

2.  In vivo dynamics of T cell activation, proliferation, and death in HIV-1 infection: why are CD4+ but not CD8+ T cells depleted?

Authors:  Ruy M Ribeiro; Hiroshi Mohri; David D Ho; Alan S Perelson
Journal:  Proc Natl Acad Sci U S A       Date:  2002-11-14       Impact factor: 11.205

3.  Estimating average cellular turnover from 5-bromo-2'-deoxyuridine (BrdU) measurements.

Authors:  Rob J De Boer; Hiroshi Mohri; David D Ho; Alan S Perelson
Journal:  Proc Biol Sci       Date:  2003-04-22       Impact factor: 5.349

4.  Explicit kinetic heterogeneity: mathematical models for interpretation of deuterium labeling of heterogeneous cell populations.

Authors:  Vitaly V Ganusov; José A M Borghans; Rob J De Boer
Journal:  PLoS Comput Biol       Date:  2010-02-05       Impact factor: 4.475

5.  Quantifying T lymphocyte turnover.

Authors:  Rob J De Boer; Alan S Perelson
Journal:  J Theor Biol       Date:  2013-01-09       Impact factor: 2.691

6.  CD4 T cell survival after intermittent interleukin-2 therapy is predictive of an increase in the CD4 T cell count of HIV-infected patients.

Authors:  Sarah W Read; Richard A Lempicki; Michele Di Mascio; Sharat Srinivasula; Rosanne Burke; William Sachau; Marjorie Bosche; Joseph W Adelsberger; Irini Sereti; Richard T Davey; Jorge A Tavel; Chiung-Yu Huang; Haleem J Issaq; Stephen D Fox; H Clifford Lane; Joseph A Kovacs
Journal:  J Infect Dis       Date:  2008-09-15       Impact factor: 5.226

7.  Model with two types of CTL regulation and experiments on CTL dynamics.

Authors:  R A Sergeev; R E Batorsky; I M Rouzine
Journal:  J Theor Biol       Date:  2009-11-12       Impact factor: 2.691

Review 8.  Mathematics in modern immunology.

Authors:  Mario Castro; Grant Lythe; Carmen Molina-París; Ruy M Ribeiro
Journal:  Interface Focus       Date:  2016-04-06       Impact factor: 3.906

9.  Memory CD4 T cell subsets are kinetically heterogeneous and replenished from naive T cells at high levels.

Authors:  Graeme Gossel; Thea Hogan; Benedict Seddon; Andrew J Yates; Daniel Cownden
Journal:  Elife       Date:  2017-03-10       Impact factor: 8.140

10.  A mechanistic model for naive CD4 T cell homeostasis in healthy adults and children.

Authors:  Tharindi Hapuarachchi; Joanna Lewis; Robin E Callard
Journal:  Front Immunol       Date:  2013-11-11       Impact factor: 7.561

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