Literature DB >> 17360353

A model of immune regulation as a consequence of randomized lymphocyte division and death times.

E D Hawkins1, M L Turner, M R Dowling, C van Gend, P D Hodgkin.   

Abstract

The magnitude of an adaptive immune response is controlled by the interplay of lymphocyte quiescence, proliferation, and apoptosis. How lymphocytes integrate receptor-mediated signals influencing these cell fates is a fundamental question for understanding this complex system. We examined how lymphocytes interleave times to divide and die to develop a mathematical model of lymphocyte growth regulation. This model provides a powerful method for fitting and analyzing fluorescent division tracking data and reveals how summing receptor-mediated kinetic changes can modify the immune response progressively from rapid tolerance induction to strong immunity. An important consequence of our results is that intrinsic variability in otherwise identical cells, usually dismissed as noise, may have evolved to be an essential feature of immune regulation.

Mesh:

Year:  2007        PMID: 17360353      PMCID: PMC1821128          DOI: 10.1073/pnas.0700026104

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  39 in total

1.  Evidence from the generation of immunoglobulin G-secreting cells that stochastic mechanisms regulate lymphocyte differentiation.

Authors:  Jhagvaral Hasbold; Lynn M Corcoran; David M Tarlinton; Stuart G Tangye; Philip D Hodgkin
Journal:  Nat Immunol       Date:  2003-11-30       Impact factor: 25.606

2.  Stochastic model of T cell proliferation: a calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival.

Authors:  Elissa K Deenick; Amanda V Gett; Philip D Hodgkin
Journal:  J Immunol       Date:  2003-05-15       Impact factor: 5.422

Review 3.  The Bcl-2 family: roles in cell survival and oncogenesis.

Authors:  Suzanne Cory; David C S Huang; Jerry M Adams
Journal:  Oncogene       Date:  2003-11-24       Impact factor: 9.867

4.  Different dynamics of CD4+ and CD8+ T cell responses during and after acute lymphocytic choriomeningitis virus infection.

Authors:  Rob J De Boer; Dirk Homann; Alan S Perelson
Journal:  J Immunol       Date:  2003-10-15       Impact factor: 5.422

5.  A general mathematical framework to model generation structure in a population of asynchronously dividing cells.

Authors:  Kalet León; Jose Faro; Jorge Carneiro
Journal:  J Theor Biol       Date:  2004-08-21       Impact factor: 2.691

6.  Transition probability and the origin of variation in the cell cycle.

Authors:  R Shields
Journal:  Nature       Date:  1977-06-23       Impact factor: 49.962

7.  Do cells cycle?

Authors:  J A Smith; L Martin
Journal:  Proc Natl Acad Sci U S A       Date:  1973-04       Impact factor: 11.205

8.  A G1 rate model accounts for cell-cycle kinetics attributed to 'transition probability'.

Authors:  L N Castor
Journal:  Nature       Date:  1980-10-30       Impact factor: 49.962

9.  The interleukin-2 T-cell system: a new cell growth model.

Authors:  D A Cantrell; K A Smith
Journal:  Science       Date:  1984-06-22       Impact factor: 47.728

10.  The rescaling method for quantifying the turnover of cell populations.

Authors:  Sergei S Pilyugin; Vitaly V Ganusov; Kaja Murali-Krishna; Rafi Ahmed; Rustom Antia
Journal:  J Theor Biol       Date:  2003-11-21       Impact factor: 2.691

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  68 in total

Review 1.  Mapping the life histories of T cells.

Authors:  Ton N M Schumacher; Carmen Gerlach; Jeroen W J van Heijst
Journal:  Nat Rev Immunol       Date:  2010-08-06       Impact factor: 53.106

Review 2.  Clonal expansion under the microscope: studying lymphocyte activation and differentiation using live-cell imaging.

Authors:  Michal Polonsky; Benjamin Chain; Nir Friedman
Journal:  Immunol Cell Biol       Date:  2015-12-22       Impact factor: 5.126

3.  Determining the expected variability of immune responses using the cyton model.

Authors:  Vijay G Subramanian; Ken R Duffy; Marian L Turner; Philip D Hodgkin
Journal:  J Math Biol       Date:  2007-11-03       Impact factor: 2.259

Review 4.  T helper cytokine patterns: defined subsets, random expression, and external modulation.

Authors:  Tim R Mosmann; James J Kobie; F Eun-Hyung Lee; Sally A Quataert
Journal:  Immunol Res       Date:  2009-02-06       Impact factor: 2.829

5.  On the impact of correlation between collaterally consanguineous cells on lymphocyte population dynamics.

Authors:  Ken R Duffy; Vijay G Subramanian
Journal:  J Math Biol       Date:  2008-10-28       Impact factor: 2.259

6.  Mathematical models for CFSE labelled lymphocyte dynamics: asymmetry and time-lag in division.

Authors:  Tatyana Luzyanina; Jovana Cupovic; Burkhard Ludewig; Gennady Bocharov
Journal:  J Math Biol       Date:  2013-12-13       Impact factor: 2.259

7.  Identifying Noise Sources governing cell-to-cell variability.

Authors:  Simon Mitchell; Alexander Hoffmann
Journal:  Curr Opin Syst Biol       Date:  2017-12-06

8.  A single-cell pedigree analysis of alternative stochastic lymphocyte fates.

Authors:  E D Hawkins; J F Markham; L P McGuinness; P D Hodgkin
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-24       Impact factor: 11.205

9.  Estimating in vivo death rates of targets due to CD8 T-cell-mediated killing.

Authors:  Vitaly V Ganusov; Rob J De Boer
Journal:  J Virol       Date:  2008-09-24       Impact factor: 5.103

Review 10.  Non-genetic cell-to-cell variability and the consequences for pharmacology.

Authors:  Mario Niepel; Sabrina L Spencer; Peter K Sorger
Journal:  Curr Opin Chem Biol       Date:  2009-10-14       Impact factor: 8.822

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