Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling
| Autor: | Raquel Hontecillas, Meghna Verma, Andrew Leber, Josep Bassaganya-Riera, Guangrui Xie, Xi Chen, Wenjing Wang, Vida Abedi |
|---|---|
| Přispěvatelé: | Industrial and Systems Engineering |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Computer science Complex system Parameter space computer.software_genre 01 natural sciences computational immunology 010305 fluids & plasmas sensitivity analysis Kriging 0103 physical sciences Sensitivity (control systems) spatio-temporal metamodeling 010306 general physics Helicobacter pylori lcsh:T57-57.97 Applied Mathematics Sobol sequence Metamodeling Computational immunology lcsh:Applied mathematics. Quantitative methods Scalability Data mining lcsh:Probabilities. Mathematical statistics lcsh:QA273-280 computer Gaussian process regression |
| Zdroj: | Frontiers in Applied Mathematics and Statistics, Vol 5 (2019) |
| Popis: | Computational 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. This 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|