TY - JOUR
T1 - Ion channel model reduction using manifold boundaries
JF - bioRxiv
DO - 10.1101/2022.03.11.483794
SP - 2022.03.11.483794
AU - Dominic G. Whittaker
AU - Jiahui Wang
AU - Joseph G. Shuttleworth
AU - Ravichandra Venkateshappa
AU - Jacob M. Kemp
AU - Thomas W. Claydon
AU - Gary R. Mirams
Y1 - 2022/01/01
UR - http://biorxiv.org/content/early/2022/05/16/2022.03.11.483794.abstract
N2 - Mathematical models of voltage-gated ion channels are used in basic research, industrial and clinical settings. These models range in complexity, but typically contain numerous variables representing the proportion of channels in a given state, and parameters describing the voltage-dependent rates of transition between states. An open problem is selecting the appropriate degree of complexity and structure for an ion channel model given data availability. Here, we simplify a model of the cardiac human Ether-à-go-go Related Gene (hERG) potassium ion channel, which carries cardiac IKr, using the manifold boundary approximation method (MBAM). The MBAM approximates high-dimensional model-output manifolds by reduced models describing their boundaries, resulting in models with fewer parameters (and often variables). We produced a series of models of reducing complexity starting from an established 5-state hERG model with 15 parameters. Models with up to 3 fewer states and 8 fewer parameters were shown to retain much of the predictive capability of the full model and were validated using experimental hERG1a data collected in HEK293 cells at 37°C. The method provides a way to simplify complex models of ion channels that improves parameter identifiability and will aid in future model development.Competing Interest StatementThe authors have declared no competing interest.
ER -