TY - JOUR
T1 - Calibrations and validations of biological models with an application on the renal fibrosis
AU - Karagiannis, Georgios
AU - Hao, Wenrui
AU - Lin, Guang
N1 - Funding Information:
W.H.'s research was supported by the American Heart Association (Grant 17SDG33660722) and the National Science Foundation (Grant DMS‐1818769). G.L. gratefully acknowledge the support from the National Science Foundation (DMS‐1555072, DMS‐1736364, and DMS‐1821233).
Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - We calibrate a mathematical model of renal tubulointerstitial fibrosis by Hao et al which is used to explore potential drugs for Lupus Nephritis, against a real data set of 84 patients. For this purpose, we present a general calibration procedure which can be used for the calibration analysis of other biological systems as well. Central to the procedure is the idea of designing a Bayesian Gaussian process (GP) emulator that can be used as a surrogate of the fibrosis mathematical model which is computationally expensive to run massively at every input value. The procedure relies on detecting influential model parameters by a GP-based sensitivity analysis, and calibrating them by specifying a maximum likelihood criterion, tailored to the application, which is optimized via Bayesian global optimization.
AB - We calibrate a mathematical model of renal tubulointerstitial fibrosis by Hao et al which is used to explore potential drugs for Lupus Nephritis, against a real data set of 84 patients. For this purpose, we present a general calibration procedure which can be used for the calibration analysis of other biological systems as well. Central to the procedure is the idea of designing a Bayesian Gaussian process (GP) emulator that can be used as a surrogate of the fibrosis mathematical model which is computationally expensive to run massively at every input value. The procedure relies on detecting influential model parameters by a GP-based sensitivity analysis, and calibrating them by specifying a maximum likelihood criterion, tailored to the application, which is optimized via Bayesian global optimization.
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U2 - 10.1002/cnm.3329
DO - 10.1002/cnm.3329
M3 - Article
C2 - 32101373
AN - SCOPUS:85081891407
SN - 2040-7939
VL - 36
JO - International Journal for Numerical Methods in Biomedical Engineering
JF - International Journal for Numerical Methods in Biomedical Engineering
IS - 5
M1 - e3329
ER -