Info! All errors must be resolved before the model can be simulated.

None

None

None

None

None

None

Title

Antigen-driven T-cell turnover.

Authors

Fraser C (1), Ferguson NM, De Wolf F, Ghani AC, Garnett GP, Anderson RM.

Affiliations

1) Department of Infectious Disease Epidemiology, Imperial College of Science, Technology and Medicine, St Mary's Campus, Norfolk Place, Paddington, London W21PG, UK. c.fraser@ic.ac.uk

Abstract

A mathematical model is developed to characterize the distribution of cell turnover rates within a population of T lymphocytes. Previous models of T-cell dynamics have assumed a constant uniform turnover rate; here we consider turnover in a cell pool subject to clonal proliferation in response to diverse and repeated antigenic stimulation. A basic framework is defined for T-cell proliferation in response to antigen, which explicitly describes the cell cycle during antigenic stimulation and subsequent cell division. The distribution of T-cell turnover rates is then calculated based on the history of random exposures to antigens. This distribution is found to be bimodal, with peaks in cell frequencies in the slow turnover (quiescent) and rapid turnover (activated) states. This distribution can be used to calculate the overall turnover for the cell pool, as well as individual contributions to turnover from quiescent and activated cells. The impact of heterogeneous turnover on the dynamics of CD4(+) T-cell infection by HIV is explored. We show that our model can resolve the paradox of high levels of viral replication occurring while only a small fraction of cells are infected.

Journal

J Theor Biol. 2002 Nov 21;219(2):177-92.

Unit definitions have no effect on the numerical analysis of the model. It remains the responsibility of the modeler to ensure the internal numerical consistency of the model. If units are provided, however, the consistency of the model units will be checked.

Name Definition
Id Name Spatial dimensions Size
default 1.0
Id Name Initial quantity Compartment Fixed
a1 1.0 default
a2 1.0 default
a3 1.0 default
x1 0.01 default
x2 0.01 default
x3 0.01 default

Initial assignments are expressions that are evaluated at time=0. It is not recommended to create initial assignments for all model entities. Restrict the use of initial assignments to cases where a value is expressed in terms of values or sizes of other model entities. Note that it is not permitted to have both an initial assignment and an assignment rule for a single model entity.

Definition
Id Name Objective coefficient Reaction Equation and Kinetic Law Flux bounds
v1 ∅ = a1

eta1*a1
v10 a3 = ∅

sigma*a3*x3
v11 ∅ = x3

mu*x3
v12 ∅ = x3

((-mu + p)*a3*x3)/(at + a3)
v13 x1 = ∅

(mu*x1^2)/gamma
v14 x2 = ∅

(mu*x2^2)/gamma
v15 x3 = ∅

(mu*x3^2)/gamma
v2 a1 = ∅

sigma*a1*x1
v3 ∅ = x1

mu*x1
v4 ∅ = x1

(a1*x1)/(at + a1)
v5 ∅ = a2

eta2*a2
v6 a2 = ∅

sigma*a2*x2
v7 ∅ = x2

mu*x2
v8 ∅ = x2

((-mu + p)*a2*x2)/(at + a2)
v9 ∅ = a3

eta3*a3

Global parameters

Id Value
at 0.0001
eta1 0.0
eta2 1.0
eta3 5.1
gamma 0.01
mu 0.001
p 1.0
sigma 0.5

Local parameters

Id Value Reaction

Assignment rules

Definition

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments