Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection
Identifieur interne : 000664 ( Ncbi/Merge ); précédent : 000663; suivant : 000665Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection
Auteurs : Maksudul Alam [États-Unis] ; Xinwei Deng [États-Unis] ; Casandra Philipson [États-Unis] ; Josep Bassaganya-Riera [États-Unis] ; Keith Bisset [États-Unis] ; Adria Carbo [États-Unis] ; Stephen Eubank [États-Unis] ; Raquel Hontecillas [États-Unis] ; Stefan Hoops [États-Unis] ; Yongguo Mei [États-Unis] ; Vida Abedi [États-Unis] ; Madhav Marathe [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Abstract
Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to
Url:
DOI: 10.1371/journal.pone.0136139
PubMed: 26327290
PubMed Central: 4556515
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<author><name sortKey="Marathe, Madhav" sort="Marathe, Madhav" uniqKey="Marathe M" first="Madhav" last="Marathe">Madhav Marathe</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to <italic>Helicobacter pylori</italic>
Infection</title>
<author><name sortKey="Alam, Maksudul" sort="Alam, Maksudul" uniqKey="Alam M" first="Maksudul" last="Alam">Maksudul Alam</name>
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<author><name sortKey="Bassaganya Riera, Josep" sort="Bassaganya Riera, Josep" uniqKey="Bassaganya Riera J" first="Josep" last="Bassaganya-Riera">Josep Bassaganya-Riera</name>
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<author><name sortKey="Bisset, Keith" sort="Bisset, Keith" uniqKey="Bisset K" first="Keith" last="Bisset">Keith Bisset</name>
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<author><name sortKey="Carbo, Adria" sort="Carbo, Adria" uniqKey="Carbo A" first="Adria" last="Carbo">Adria Carbo</name>
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<author><name sortKey="Eubank, Stephen" sort="Eubank, Stephen" uniqKey="Eubank S" first="Stephen" last="Eubank">Stephen Eubank</name>
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<author><name sortKey="Hontecillas, Raquel" sort="Hontecillas, Raquel" uniqKey="Hontecillas R" first="Raquel" last="Hontecillas">Raquel Hontecillas</name>
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<author><name sortKey="Hoops, Stefan" sort="Hoops, Stefan" uniqKey="Hoops S" first="Stefan" last="Hoops">Stefan Hoops</name>
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<author><name sortKey="Mei, Yongguo" sort="Mei, Yongguo" uniqKey="Mei Y" first="Yongguo" last="Mei">Yongguo Mei</name>
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<author><name sortKey="Abedi, Vida" sort="Abedi, Vida" uniqKey="Abedi V" first="Vida" last="Abedi">Vida Abedi</name>
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</nlm:aff>
<country xml:lang="fr">États-Unis</country>
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</nlm:aff>
<country xml:lang="fr">États-Unis</country>
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<placeName><region type="state">Virginie</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Marathe, Madhav" sort="Marathe, Madhav" uniqKey="Marathe M" first="Madhav" last="Marathe">Madhav Marathe</name>
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</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea>
<placeName><region type="state">Virginie</region>
</placeName>
</affiliation>
<affiliation wicri:level="2"><nlm:aff id="aff003"><addr-line>Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea>
<placeName><region type="state">Virginie</region>
</placeName>
</affiliation>
<affiliation wicri:level="2"><nlm:aff id="aff005"><addr-line>Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</nlm:aff>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Department of Computer Science, Virginia Tech, Blacksburg, Virginia</wicri:regionArea>
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<series><title level="j">PLoS ONE</title>
<idno type="eISSN">1932-6203</idno>
<imprint><date when="2015">2015</date>
</imprint>
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<front><div type="abstract" xml:lang="en"><p>Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to <italic>Helicobacter pylori</italic>
colonization of the gastric mucosa.</p>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id>
<journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosone</journal-id>
<journal-title-group><journal-title>PLoS ONE</journal-title>
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<issn pub-type="epub">1932-6203</issn>
<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, CA USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26327290</article-id>
<article-id pub-id-type="pmc">4556515</article-id>
<article-id pub-id-type="publisher-id">PONE-D-15-10087</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0136139</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to <italic>Helicobacter pylori</italic>
Infection</article-title>
<alt-title alt-title-type="running-head">Sensitivity Analysis of ENISI-Based Model</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Alam</surname>
<given-names>Maksudul</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff005"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Deng</surname>
<given-names>Xinwei</given-names>
</name>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff006"><sup>6</sup>
</xref>
<xref ref-type="corresp" rid="cor001">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Philipson</surname>
<given-names>Casandra</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Bassaganya-Riera</surname>
<given-names>Josep</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff004"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Bisset</surname>
<given-names>Keith</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Carbo</surname>
<given-names>Adria</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Eubank</surname>
<given-names>Stephen</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hontecillas</surname>
<given-names>Raquel</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Hoops</surname>
<given-names>Stefan</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mei</surname>
<given-names>Yongguo</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Abedi</surname>
<given-names>Vida</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Marathe</surname>
<given-names>Madhav</given-names>
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<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff005"><sup>5</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</aff>
<aff id="aff003"><label>3</label>
<addr-line>Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</aff>
<aff id="aff004"><label>4</label>
<addr-line>Department of Biomedical Sciences and Pathobiology, Virginia-Maryland Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</aff>
<aff id="aff005"><label>5</label>
<addr-line>Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</aff>
<aff id="aff006"><label>6</label>
<addr-line>Department of Statistics, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Castiglione</surname>
<given-names>Filippo</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>National Research Council of Italy (CNR), ITALY</addr-line>
</aff>
<author-notes><fn fn-type="conflict" id="coi001"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: MA XD MM. Performed the experiments: MA XD. Analyzed the data: MA XD CP AC RH JBR MM. Contributed reagents/materials/analysis tools: SE KB SH YM VA. Wrote the paper: MA XD VA RH MM.</p>
</fn>
<corresp id="cor001">* E-mail: <email>xdeng@vt.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>1</day>
<month>9</month>
<year>2015</year>
</pub-date>
<volume>10</volume>
<issue>9</issue>
<elocation-id>e0136139</elocation-id>
<history><date date-type="received"><day>7</day>
<month>3</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>31</day>
<month>7</month>
<year>2015</year>
</date>
</history>
<permissions><copyright-year>2015</copyright-year>
<copyright-holder>Alam et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pone.0136139.pdf"></self-uri>
<abstract><p>Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to <italic>Helicobacter pylori</italic>
colonization of the gastric mucosa.</p>
</abstract>
<funding-group><funding-statement>This work has been partially supported by Defense Threat Reduction Agency (DTRA)—R&D (HPC) Award No. HDTRA1-09-1-0017, DTRA—Validation Award No. HDTRA1-11-1-0016, DTRA—Comprehensive National Incident Management System (CNIMS) Award No. HDRTA1-11-D-0016-0001, National Science Foundation (NSF) PetaApps Grant OCI-0904844, NSF Network Science and Engineering (NetSE) Grant CNS-1011769, NSF Software Development for Cyberinfrastructure (SDCI) Grant OCI-1032677, National Institutes of Health (NIH) MIDAS project 2U01GM070694-7, and National Institute of Allergy and Infectious Diseases (NIAID) & NIH project HHSN272201000056C.</funding-statement>
</funding-group>
<counts><fig-count count="12"></fig-count>
<table-count count="4"></table-count>
<page-count count="25"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>The data can be obtained without restriction after creating an account from: <ext-link ext-link-type="uri" xlink:href="https://immport.niaid.nih.gov/immportWeb/clinical/study/displayStudyDetails.do?itemList=SDY230">https://immport.niaid.nih.gov/immportWeb/clinical/study/displayStudyDetails.do?itemList=SDY230</ext-link>
. The Study number is SDY230.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>The data can be obtained without restriction after creating an account from: <ext-link ext-link-type="uri" xlink:href="https://immport.niaid.nih.gov/immportWeb/clinical/study/displayStudyDetails.do?itemList=SDY230">https://immport.niaid.nih.gov/immportWeb/clinical/study/displayStudyDetails.do?itemList=SDY230</ext-link>
. The Study number is SDY230.</p>
</notes>
</front>
</pmc>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Virginie</li>
</region>
</list>
<tree><country name="États-Unis"><region name="Virginie"><name sortKey="Alam, Maksudul" sort="Alam, Maksudul" uniqKey="Alam M" first="Maksudul" last="Alam">Maksudul Alam</name>
</region>
<name sortKey="Abedi, Vida" sort="Abedi, Vida" uniqKey="Abedi V" first="Vida" last="Abedi">Vida Abedi</name>
<name sortKey="Abedi, Vida" sort="Abedi, Vida" uniqKey="Abedi V" first="Vida" last="Abedi">Vida Abedi</name>
<name sortKey="Alam, Maksudul" sort="Alam, Maksudul" uniqKey="Alam M" first="Maksudul" last="Alam">Maksudul Alam</name>
<name sortKey="Alam, Maksudul" sort="Alam, Maksudul" uniqKey="Alam M" first="Maksudul" last="Alam">Maksudul Alam</name>
<name sortKey="Bassaganya Riera, Josep" sort="Bassaganya Riera, Josep" uniqKey="Bassaganya Riera J" first="Josep" last="Bassaganya-Riera">Josep Bassaganya-Riera</name>
<name sortKey="Bassaganya Riera, Josep" sort="Bassaganya Riera, Josep" uniqKey="Bassaganya Riera J" first="Josep" last="Bassaganya-Riera">Josep Bassaganya-Riera</name>
<name sortKey="Bassaganya Riera, Josep" sort="Bassaganya Riera, Josep" uniqKey="Bassaganya Riera J" first="Josep" last="Bassaganya-Riera">Josep Bassaganya-Riera</name>
<name sortKey="Bisset, Keith" sort="Bisset, Keith" uniqKey="Bisset K" first="Keith" last="Bisset">Keith Bisset</name>
<name sortKey="Bisset, Keith" sort="Bisset, Keith" uniqKey="Bisset K" first="Keith" last="Bisset">Keith Bisset</name>
<name sortKey="Carbo, Adria" sort="Carbo, Adria" uniqKey="Carbo A" first="Adria" last="Carbo">Adria Carbo</name>
<name sortKey="Carbo, Adria" sort="Carbo, Adria" uniqKey="Carbo A" first="Adria" last="Carbo">Adria Carbo</name>
<name sortKey="Deng, Xinwei" sort="Deng, Xinwei" uniqKey="Deng X" first="Xinwei" last="Deng">Xinwei Deng</name>
<name sortKey="Deng, Xinwei" sort="Deng, Xinwei" uniqKey="Deng X" first="Xinwei" last="Deng">Xinwei Deng</name>
<name sortKey="Eubank, Stephen" sort="Eubank, Stephen" uniqKey="Eubank S" first="Stephen" last="Eubank">Stephen Eubank</name>
<name sortKey="Eubank, Stephen" sort="Eubank, Stephen" uniqKey="Eubank S" first="Stephen" last="Eubank">Stephen Eubank</name>
<name sortKey="Hontecillas, Raquel" sort="Hontecillas, Raquel" uniqKey="Hontecillas R" first="Raquel" last="Hontecillas">Raquel Hontecillas</name>
<name sortKey="Hontecillas, Raquel" sort="Hontecillas, Raquel" uniqKey="Hontecillas R" first="Raquel" last="Hontecillas">Raquel Hontecillas</name>
<name sortKey="Hoops, Stefan" sort="Hoops, Stefan" uniqKey="Hoops S" first="Stefan" last="Hoops">Stefan Hoops</name>
<name sortKey="Hoops, Stefan" sort="Hoops, Stefan" uniqKey="Hoops S" first="Stefan" last="Hoops">Stefan Hoops</name>
<name sortKey="Marathe, Madhav" sort="Marathe, Madhav" uniqKey="Marathe M" first="Madhav" last="Marathe">Madhav Marathe</name>
<name sortKey="Marathe, Madhav" sort="Marathe, Madhav" uniqKey="Marathe M" first="Madhav" last="Marathe">Madhav Marathe</name>
<name sortKey="Marathe, Madhav" sort="Marathe, Madhav" uniqKey="Marathe M" first="Madhav" last="Marathe">Madhav Marathe</name>
<name sortKey="Mei, Yongguo" sort="Mei, Yongguo" uniqKey="Mei Y" first="Yongguo" last="Mei">Yongguo Mei</name>
<name sortKey="Mei, Yongguo" sort="Mei, Yongguo" uniqKey="Mei Y" first="Yongguo" last="Mei">Yongguo Mei</name>
<name sortKey="Philipson, Casandra" sort="Philipson, Casandra" uniqKey="Philipson C" first="Casandra" last="Philipson">Casandra Philipson</name>
<name sortKey="Philipson, Casandra" sort="Philipson, Casandra" uniqKey="Philipson C" first="Casandra" last="Philipson">Casandra Philipson</name>
</country>
</tree>
</affiliations>
</record>
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