Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling

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dc.contributor.authorChen, Xien
dc.contributor.authorWang, Wenjingen
dc.contributor.authorXie, Guangruien
dc.contributor.authorHontecillas, Raquelen
dc.contributor.authorVerma, Meghnaen
dc.contributor.authorLeber, Andrewen
dc.contributor.authorBassaganya-Riera, Josepen
dc.contributor.authorAbedi, Vidaen
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2019-07-24T17:18:54Zen
dc.date.available2019-07-24T17:18:54Zen
dc.date.issued2019-02-05en
dc.description.abstractComputational immunology studies the interactions between the components of the immune system that includes the interplay between regulatory and inflammatory elements. It provides a solid framework that aids the conversion of pre-clinical and clinical data into mathematical equations to enable modeling and in silico experimentation. The modeling-driven insights shed lights on some of the most pressing immunological questions and aid the design of fruitful validation experiments. A typical system of equations, mapping the interaction among various immunological entities and a pathogen, consists of a high-dimensional input parameter space that could drive the stochastic system outputs in unpredictable directions. In this paper, we perform spatio-temporal metamodel-based sensitivity analysis of immune response to Helicobacter pylori infection using the computational model developed by the ENteric Immune SImulator (ENISI). We propose a two-stage metamodel-based procedure to obtain the estimates of the Sobol’ total and first-order indices for each input parameter, for quantifying their time-varying impacts on each output of interest. In particular, we fully reuse and exploit information from an existing simulated dataset, develop a novel sampling design for constructing the two-stage metamodels, and perform metamodel-based sensitivity analysis. The proposed procedure is scalable, easily interpretable, and adaptable to any multi-input multi-output complex systems of equations with a high-dimensional input parameter space.en
dc.description.sponsorshipThis work was supported by funds to XC from ICTAS Junior Faculty Award (No. 176371), the Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Geisinger Health System, as well as funds from the Defense Threat Reduction Agency (DTRA) to JB-R and RH (Virginia Tech, HDTRA1-18-1-0008), and to VA (Sub-PI, Geisinger, Subaward No. 450557-19D03).en
dc.format.extent15 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationChen X, Wang W, Xie G, Hontecillas R, Verma M, Leber A, Bassaganya-Riera J and Abedi V (2019) Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling. Front. Appl. Math. Stat. 5:4. doi: 10.3389/fams.2019.00004en
dc.identifier.doihttps://doi.org/10.3389/fams.2019.00004en
dc.identifier.issue4en
dc.identifier.urihttp://hdl.handle.net/10919/91947en
dc.identifier.volume5en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectcomputational immunologyen
dc.subjectGaussian process regressionen
dc.subjectHelicobacter pylorien
dc.subjectsensitivity analysisen
dc.subjectspatio-temporal metamodelingen
dc.titleMulti-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodelingen
dc.title.serialFrontiers in Applied Mathematics and Statisticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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