Stochastic modeling of naïve T cell homeostasis for competing clonotypes VIA the master equation

Macnamara, S. and Burrage, K. (2010) Stochastic modeling of naïve T cell homeostasis for competing clonotypes VIA the master equation. Multiscale Modeling and Simulation, 8 4: 1325-1347. doi:10.1137/09077182X

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ217113_OA.pdf Full text (open access) application/pdf 3.60MB 0

Author Macnamara, S.
Burrage, K.
Title Stochastic modeling of naïve T cell homeostasis for competing clonotypes VIA the master equation
Journal name Multiscale Modeling and Simulation   Check publisher's open access policy
ISSN 1540-3459
1540-3467
Publication date 2010
Sub-type Article (original research)
DOI 10.1137/09077182X
Open Access Status File (Publisher version)
Volume 8
Issue 4
Start page 1325
End page 1347
Total pages 23
Place of publication Philadelphia, PA, United States
Publisher Society for Industrial and Applied Mathematics
Collection year 2011
Language eng
Abstract Stochastic models for competing clonotypes of T cells by multivariate, continuoustime, discrete state, Markov processes have recently been proposed in the literature. A stochastic modeling framework is important because of rare events associated with small populations of some critical cell types. Usually, computational methods for these problems employ a trajectory-based approach, based on Monte Carlo simulation. This is partly because the complementary, probabilitydensity function (PDF) approaches can be expensive, but here we describe some efficient PDF approaches by directly solving the governing equations, known as the Master Equation. These computations are made very efficient through an approximation of the state space by projections and through the use of Krylov subspace methods when evolving the matrix exponential. These computational methods allow us to explore the evolution of the PDFs associated with these stochastic models, and bimodal distributions arise. Both experimental and theoretical investigations have emphasized the need to take into account effects due to aging. Thus time-dependent propensities naturally arise in immunological processes. Incorporating time-dependent propensities into the framework of the Master Equation significantly complicates the corresponding computational methods, but here we describe an efficient approach via Magnus formulas. Although this contribution focuses on the example of competing clonotypes, the general principles are relevant to multivariate Markov processes and provide fundamental techniques for computational immunology. © 2010 Society for Industrial and Applied Mathematics.
Keyword Immunology
Systems biology
Master equation
Magnus expansion
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
Institute for Molecular Bioscience - Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 4 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sun, 26 Sep 2010, 00:02:44 EST