<?xml version="1.0"?>
<!-- FILE : rong_2009a.cellml
CREATED : 4th Feb 2010
LAST MODIFIED : 4th Feb 2010
AUTHOR : Ethan Choi
MODEL STATUS : This model conforms to the CellML 1.1 Specification. Runs in OpenCell
DESCRIPTION : This file contains a CellML description of Rong's 2009 mathematical model-->
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<!-- documentation -->
<documentation xmlns="http://cellml.org/tmp-documentation">
<article>
<articleinfo>
<title>Modeling latently infected cell activation: viral and latent reservoir persistence, and viral blips in HIV-infected patients on potent therapy</title>
<author>
<firstname>Ethan</firstname>
<surname>Choi</surname>
<affiliation>
<shortaffil>Auckland Bioengineering Institute, The University of Auckland</shortaffil>
</affiliation>
</author>
</articleinfo>
<section id="sec_status">
<title>Model Status</title>
<para>This CellML model has been built with the differential expressions in Rong and Perelson's 2009 paper for the initial basic model of latent cell activation using the (1) equations (rong_2009a). This file is known to run in OpenCell and COR, the units have been checked and they are consistent, and the parameter values of the CellML model were taken from Table 1 in the paper. For these values the critical efficacy = 0.5208 so efficacy has been set to 0.4. Unfortunately the simulation output of this CellML model cannot be fully verified as there are no figures in the published paper for the initial model (equations (1)).</para>
</section>
<sect1 id="sec_structure">
<title>Model Structure</title>
<para>ABSTRACT: Although potent combination therapy is usually able to suppress plasma viral loads in HIV-1 patients to below the detection limit of conventional clinical assays, a low level of viremia frequently can be detected in plasma by more sensitive assays. Additionally, many patients experience transient episodes of viremia above the detection limit, termed viral blips, even after being on highly suppressive therapy for many years. An obstacle to viral eradication is the persistence of a latent reservoir for HIV-1 in resting memory CD4(+) T cells. The mechanisms underlying low viral load persistence, slow decay of the latent reservoir, and intermittent viral blips are not fully characterized. The quantitative contributions of residual viral replication to viral and the latent reservoir persistence remain unclear. In this paper, we probe these issues by developing a mathematical model that considers latently infected cell activation in response to stochastic antigenic stimulation. We demonstrate that programmed expansion and contraction of latently infected cells upon immune activation can generate both low-level persistent viremia and intermittent viral blips. Also, a small fraction of activated T cells revert to latency, providing a potential to replenish the latent reservoir. By this means, occasional activation of latently infected cells can explain the variable decay characteristics of the latent reservoir observed in different clinical studies. Finally, we propose a phenomenological model that includes a logistic term representing homeostatic proliferation of latently infected cells. The model is simple but can robustly generate the multiphasic viral decline seen after initiation of therapy, as well as low-level persistent viremia and intermittent HIV-1 blips. Using these models, we provide a quantitative and integrated prospective into the long-term dynamics of HIV-1 and the latent reservoir in the setting of potent antiretroviral therapy.</para>
<informalfigure float="0" id="fig_reaction_diagram">
<mediaobject>
<imageobject>
<objectinfo>
<title>model diagram</title>
</objectinfo>
<imagedata fileref="rong_2009.png"/>
</imageobject>
</mediaobject>
<caption>Schematic representation of the model with latently infected cell activation.</caption>
</informalfigure>
<para>The original paper reference is cited below:</para>
<para>Modeling latently infected cell activation: viral and latent reservoir persistence, and viral blips in HIV-infected patients on potent therapy, Rong L, Perelson A, 2009, <emphasis>PLoS Computational Biology</emphasis>, 5, (issue 10): e1000533. <ulink url="http://www.ncbi.nlm.nih.gov/pubmed/19834532">PubMed ID: 19834532</ulink>
</para>
</sect1>
</article>
</documentation>
<!-- units -->
<units name="day">
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</units>
<units name="per_day">
<unit exponent="-1" units="day"/>
</units>
<units name="ml">
<unit prefix="milli" units="litre"/>
</units>
<units name="ml_per_day">
<unit units="ml"/>
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<units name="per_ml_day">
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<unit exponent="-1" units="day"/>
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<units name="per_ml">
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<!-- components -->
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</rdf:RDF>
<variable name="time" public_interface="in" units="day"/>
<variable name="lambda" initial_value="1E4" units="per_ml_day"/>
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<apply>
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<component name="latently_infected" cmeta:id="latently_infected">
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<rdf:Description rdf:about="#latently_infected">This component relates to the latently infected CD4+ cell dynamics</rdf:Description>
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<ci>eta</ci>
<apply>
<minus/>
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<ci>efficacy</ci>
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<ci>a_L</ci>
<ci>L</ci>
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<component name="productively_infected" cmeta:id="productively_infected">
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<rdf:Description rdf:about="#productively_infected">This component relates to the productively infected cells' dynamics</rdf:Description>
<rdf:Description rdf:about="#T_star">productively infected cells that can produce virus particles (T* in the paper).</rdf:Description>
</rdf:RDF>
<variable name="time" public_interface="in" units="day"/>
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<variable name="eta" public_interface="in" units="dimensionless"/>
<variable name="efficacy" public_interface="in" units="dimensionless"/>
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<variable name="T" public_interface="in" units="per_ml"/>
<variable name="L" public_interface="in" units="per_ml"/>
<variable name="V" public_interface="in" units="per_ml"/>
<variable name="T_star" cmeta:id="T_star" initial_value="0.3" public_interface="out" units="per_ml"/>
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<apply>
<times/>
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<minus/>
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<ci>eta</ci>
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<minus/>
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<ci>efficacy</ci>
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<ci>V</ci>
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<apply>
<times/>
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<apply>
<times/>
<ci>a_L</ci>
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</component>
<component name="viral_load" cmeta:id="viral_load">
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:cmeta="http://www.cellml.org/metadata/1.0#">
<rdf:Description rdf:about="#viral_load">This component relates to the HIV population size</rdf:Description>
<rdf:Description rdf:about="#V">total viral load.</rdf:Description>
</rdf:RDF>
<variable name="time" public_interface="in" units="day"/>
<variable name="N" initial_value="2000" units="dimensionless"/>
<variable name="c" initial_value="23" units="per_day"/>
<variable name="delta" public_interface="in" units="per_day"/>
<variable name="T_star" public_interface="in" units="per_ml"/>
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<ci>T_star</ci>
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<times/>
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</apply>
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</component>
<component name="drug_efficacy">
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</component>
<!-- connections -->
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<map_variables variable_1="time" variable_2="time"/>
</connection>
<connection>
<map_components component_1="environment" component_2="latently_infected"/>
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<connection>
<map_components component_1="environment" component_2="productively_infected"/>
<map_variables variable_1="time" variable_2="time"/>
</connection>
<connection>
<map_components component_1="environment" component_2="viral_load"/>
<map_variables variable_1="time" variable_2="time"/>
</connection>
<connection>
<map_components component_1="uninfected" component_2="latently_infected"/>
<map_variables variable_1="T" variable_2="T"/>
<map_variables variable_1="k" variable_2="k"/>
</connection>
<connection>
<map_components component_1="uninfected" component_2="productively_infected"/>
<map_variables variable_1="T" variable_2="T"/>
<map_variables variable_1="k" variable_2="k"/>
</connection>
<connection>
<map_components component_1="uninfected" component_2="viral_load"/>
<map_variables variable_1="V" variable_2="V"/>
</connection>
<connection>
<map_components component_1="latently_infected" component_2="viral_load"/>
<map_variables variable_1="V" variable_2="V"/>
</connection>
<connection>
<map_components component_1="productively_infected" component_2="viral_load"/>
<map_variables variable_1="V" variable_2="V"/>
<map_variables variable_1="T_star" variable_2="T_star"/>
<map_variables variable_1="delta" variable_2="delta"/>
</connection>
<connection>
<map_components component_1="latently_infected" component_2="productively_infected"/>
<map_variables variable_1="L" variable_2="L"/>
<map_variables variable_1="eta" variable_2="eta"/>
<map_variables variable_1="a_L" variable_2="a_L"/>
</connection>
<connection>
<map_components component_1="drug_efficacy" component_2="uninfected"/>
<map_variables variable_1="efficacy" variable_2="efficacy"/>
</connection>
<connection>
<map_components component_1="drug_efficacy" component_2="latently_infected"/>
<map_variables variable_1="efficacy" variable_2="efficacy"/>
</connection>
<connection>
<map_components component_1="drug_efficacy" component_2="productively_infected"/>
<map_variables variable_1="efficacy" variable_2="efficacy"/>
</connection>
<!-- metadata -->
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<dc:title xmlns:dc="http://purl.org/dc/elements/1.1/">Modeling latently infected cell activation: viral and latent reservoir persistence, and viral blips in HIV-infected patients on potent therapy (Basic Model)</dc:title>
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<dc:title xmlns:dc="http://purl.org/dc/elements/1.1/">Modeling latently infected cell activation: viral and latent reservoir persistence, and viral blips in HIV-infected patients on potent therapy</dc:title>
<cmeta:comment rdf:parseType="Resource">
<rdf:value>This is the CellML description of Rong and Perelson's 2009 mathematical model for latently infected cell activation in HIV-infected patients on potent therapy</rdf:value>
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<vCard:FN xmlns:vCard="http://www.w3.org/2001/vcard-rdf/3.0#">Ethan Choi</vCard:FN>
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<cmeta:bio_entity>CD4+ T cell</cmeta:bio_entity>
<!-- Keywords -->
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<rdf:li>dynamics</rdf:li>
<rdf:li>HAART</rdf:li>
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<dc:title xmlns:dc="http://purl.org/dc/elements/1.1/">Modeling latently infected cell activation: viral and latent reservoir persistence, and viral blips in HIV-infected patients on potent therapy</dc:title>
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<!-- OpenCell metadata -->
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