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Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

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dc.contributor.authorAlam, Maksudulen
dc.contributor.authorDeng, Xinweien
dc.contributor.authorPhilipson, Casandraen
dc.contributor.authorBassaganya-Riera, Josepen
dc.contributor.authorBisset, Keith R.en
dc.contributor.authorCarbo, Adriaen
dc.contributor.authorEubank, Stephenen
dc.contributor.authorHontecillas, Raquelen
dc.contributor.authorHoops, Stefanen
dc.contributor.authorMei, Yongguoen
dc.contributor.authorAbedi, Vidaen
dc.contributor.authorMarathe, Madhaven
dc.date.accessioned2018-09-18T14:41:00Zen
dc.date.available2018-09-18T14:41:00Zen
dc.date.issued2015-09-01en
dc.description.abstractAgent-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 Helicobacter pylori colonization of the gastric mucosa.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0136139en
dc.identifier.eissn1932-6203en
dc.identifier.issue9en
dc.identifier.othere0136139en
dc.identifier.pmid26327290en
dc.identifier.urihttp://hdl.handle.net/10919/85035en
dc.identifier.volume10en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleSensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infectionen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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