bajaria1

The model reproduces Fig 2 of the paper.

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Dynamics of naive and memory CD4+ T lymphocytes in HIV-1 disease progression.

  • Seema H Bajaria
  • Glenn Webb
  • Miles Cloyd
  • Denise Kirschner
J. Acquir. Immune Defic. Syndr. 2002; 30 (1): 41
Abstract
Understanding the dynamics of naive and memory CD4+ T cells in the immune response to HIV-1 infection can help elucidate typical disease progression patterns observed in HIV-1 patients. Although infection markers such as CD4+ T-cell count and viral load are monitored in patient blood, the lymphatic tissues (LT) have been shown to be an important viral reservoir. Here, we introduce the first comprehensive theoretical model of disease progression based on T-cell subsets and virus circulating between the two compartments of LT and blood. We use this model to predict several trademarks observed in adult HIV-1 disease progression such as the establishment of a setpoint in the asymptomatic stage. Our model predicts that both host and viral elements play a role in determining different disease progression patterns. Viral factors include viral infectivity and production rates, whereas host factors include elements of specific immunity. We also predict the effect of highly active antiretroviral therapy and treatment cessation on cellular and viral dynamics in both blood and LT.

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
MB 550.0 default
ML 110000000000.0 default
NB 450.0 default
NL 90000000000.0 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 ∅ = NB

beta*eNL*NL
v10 ML = ∅

eML*ML
v11 ML = ∅

muM*ML
v12 ∅ = NL

90000000000*0.97^(time/365)*muN
v13 ∅ = ML

R/(K1 + NL)
v2 NB = ∅

eNB*NB
v3 ∅ = MB

beta*eML*ML
v4 MB = ∅

eMB*MB
v5 ∅ = NL

alpha*eNB*NB
v6 NL = ∅

eNL*NL
v7 NL = ∅

muN*NL
v8 ∅ = ML

lambda*muN*NL
v9 ∅ = ML

alpha*eMB*MB

Global parameters

Id Value
K1 100000000000.0
R 65000000000000000000
SUMB 0.0
SUML 0.0
alpha 5000000.0
beta 0.0000002
eMB 10.0
eML 0.25
eNB 40.0
eNL 1.0
lambda 0.1
muM 0.003
muN 0.002

Local parameters

Id Value Reaction

Assignment rules

Definition
SUMB = MB + NB
SUML = ML + NL

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments