<?xml version="1.0" encoding="utf-8"?>
<!--
This CellML file was generated on 12/02/2010 at 2:20:38 at p.m. using:
COR (0.9.31.1333)
Copyright 2002-2010 Dr Alan Garny
http://cor.physiol.ox.ac.uk/ - cor@physiol.ox.ac.uk
CellML 1.0 was used to generate this model
http://www.cellml.org/
-->
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<title>A model of HIV-1 pathogenesis that includes an intracellular delay</title>
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<firstname>Catherine</firstname>
<surname>Lloyd</surname>
<affiliation>
<shortaffil>Auckland Bioengineering Institute, The University of Auckland</shortaffil>
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<section id="sec_status">
<title>Model Status</title>
<para>
This CellML model represents the general model from the paper, based on equation 1. The CellML model runs in both COR and OpenCell and the units are consistent. The model simulation output looks reasonable but we are unsure as to whether or not it recreates the results of the published model as there are no obvious figures for simple comparison.
</para>
</section>
<sect1 id="sec_structure">
<title>Model Structure</title>
<para>
ABSTRACT: Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection..
</para>
<para>
The original paper reference is cited below:
</para>
<para>
A model of HIV-1 pathogenesis that includes an intracellular delay, Patrick W. Nelson, James D. Murray, and Alan S. Perelson, 2000, <emphasis>Mathematical Biosciences</emphasis>, 163, 201-215. <ulink url="http://www.ncbi.nlm.nih.gov/pubmed/10701304">PubMed ID: 10701304</ulink>
</para>
<informalfigure float="0" id="fig_reaction_diagram">
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<title>reaction schematic for the model</title>
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<caption>A schematic diagram showing the cascade of events triggered by the binding of a HIV-1 virus particle to a receptor on a target T-cell.</caption>
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</sect1>
</article>
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The University of Auckland, Bioengineering Institute
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This is the CellML description of Nelson et al's 2000 mathematical
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Added publication date information.
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Nelson et al's 2000 mathematical model of HIV-1 pathogenesis.
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A model of HIV-1 pathogenesis that includes an intracellular delay
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Corrected syntax error with base_units
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<dcterms:W3CDTF>2005-04-20</dcterms:W3CDTF>
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<map_components component_1="infected_T_cells" component_2="infectious_virus"/>
<map_variables variable_1="T_star" variable_2="T_star"/>
<map_variables variable_1="VI" variable_2="VI"/>
</connection>
<connection>
<map_components component_1="infected_T_cells" component_2="non_infectious_virus"/>
<map_variables variable_1="T_star" variable_2="T_star"/>
</connection>
<connection>
<map_components component_1="uninfected_T_cells" component_2="infectious_virus"/>
<map_variables variable_1="VI" variable_2="VI"/>
</connection>
<connection>
<map_components component_1="total_virus" component_2="non_infectious_virus"/>
<map_variables variable_1="VNI" variable_2="VNI"/>
</connection>
<connection>
<map_components component_1="total_virus" component_2="infectious_virus"/>
<map_variables variable_1="VI" variable_2="VI"/>
</connection>
</model>
