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A Local Sensitivity Analysis Method for Developing Biological Models with Identifiable Parameters: Application to Cardiac Ionic Channel Modelling

Authors :
Philip Maybank
Andrew J. Wathen
Denis Noble
Anna A. Sher
Ken Wang
David Abramson
Gary R. Mirams
David J. Gavaghan
Source :
Future Generation Computer Systems. 29(2)
Publication Year :
2013

Abstract

Computational cardiac models provide important insights into the underlying mechanisms of heart function. Parameter estimation in these models is an ongoing challenge with many existing models being overparameterised. Sensitivity analysis presents a key tool for exploring the parameter identifiability. While existing methods provide insights into the significance of the parameters, they are unable to identify redundant parameters in an efficient manner. We present a new singular value decomposition based algorithm for determining parameter identifiability in cardiac models. Using this local sensitivity approach, we investigate the Ten Tusscher 2004 rapid inward rectifier potassium and the Mahajan 2008 rabbit L-type calcium currents in ventricular myocyte models. We identify non-significant and redundant parameters and improve the models by reducing them to minimum ones that are validated to have only identifiable parameters. The newly proposed approach provides a new method for model validation and evaluation of the predictive power of cardiac models. Highlights? New local sensitivity approach for determining parameter identifiability is presented. ? Non-significant and redundant parameters in ionic cardiac models of human and rabbit ventricular myocytes are identified. ? Application of the newly developed method to cardiac models' development and removal of the issue of overparameterisation is demonstrated.

Details

Volume :
29
Issue :
2
Database :
OpenAIRE
Journal :
Future Generation Computer Systems
Accession number :
edsair.doi.dedup.....291ecbf6065ae2de8c441921ebeee9d9