TY - GEN
T1 - Global sensitivity analysis for developing biological models
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
AU - Du, Yuncheng
AU - Du, Dongping
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Mathematical models of cardiac myocytes are highly nonlinear and involve a large number of model parameters. The parameters are estimated using experimental data, which are often corrupted by noise and uncertainty. Such uncertainty can be propagated onto model parameters during model calibration, which further affects model reliability and credibility. In order to improve model accuracy, it is important to quantify and reduce the uncertainty in model response resulting from parametric uncertainty. Sensitivity analysis is a key technique to investigate the significance of parametric uncertainty and its effect on model responses. This can identify and rank most sensitive parameters, and evaluate the effect of uncertainty on model outputs. In this work, a global sensitivity analysis is developed to determine the significance of parametric uncertainty on model responses using Sobol indices. This method is applied to nonlinear K+ channel models of mouse ventricular myocytes to demonstrate the efficacy of the developed algorithm.
AB - Mathematical models of cardiac myocytes are highly nonlinear and involve a large number of model parameters. The parameters are estimated using experimental data, which are often corrupted by noise and uncertainty. Such uncertainty can be propagated onto model parameters during model calibration, which further affects model reliability and credibility. In order to improve model accuracy, it is important to quantify and reduce the uncertainty in model response resulting from parametric uncertainty. Sensitivity analysis is a key technique to investigate the significance of parametric uncertainty and its effect on model responses. This can identify and rank most sensitive parameters, and evaluate the effect of uncertainty on model outputs. In this work, a global sensitivity analysis is developed to determine the significance of parametric uncertainty on model responses using Sobol indices. This method is applied to nonlinear K+ channel models of mouse ventricular myocytes to demonstrate the efficacy of the developed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85032228620&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8037655
DO - 10.1109/EMBC.2017.8037655
M3 - Conference contribution
C2 - 29060696
AN - SCOPUS:85032228620
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3676
EP - 3679
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 July 2017 through 15 July 2017
ER -