Four Ways to Fit an Ion Channel Model

Abstract

Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.

Neural Network Differential Equations For Ion Channel Modelling

“…That is, we believe the training space should cover the full dynamics of interest within the state space, such that when we use the model to perform "predictions, " we are predicting a different state space trajectory within, or very close to, the trained state space. Note that Figure 7 also shows that a mechanistic model (candidate model, blue) fitted to Pr3 and Pr4 would give "reasonable" predictions for Pr5, although not as good as those in Figure 5 (see Clerx et al, 2019a). This performance is thought to be due to the mechanistic equations appropriately restricting model predictions-resulting in far more reliable and biophysicallybased extrapolation.…”

“…It is worth noting that large biophysicallyinspired models could also run into the same overfitting issue (Whittaker et al, 2020). Clerx et al (2019a) compared the performances of using conventional protocols (such as Pr3, Pr4, and Pr5) and using Comparing the original candidate model, the a-gate modelled using a neural network (NNf), and the a-gate with a neural network discrepancy term (NN-d) for training results: the activation steady-state protocol (Pr3), and the deactivation time constant protocol (Pr5); and the prediction results: the inactivation protocol (Pr4), the sinusoidal protocol, and the action potential protocol (APs).…”

Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models

“…Within ion channel modelling, the kinetic parameters need to be estimated. For any two-gate Hodgkin–Huxley model, there are four ways to fit a voltage-dependent ion-channel model to whole-cell current experiments [ 76 ]. The first method is to use the voltage clamp protocol to measure the ion current and analyze it to obtain the time constant and steady state of multiple voltages.…”

Reducing complexity and unidentifiability when modelling human atrial cells

“…The width was increased if it was noted that during calibration the distribution was being restricted by the upper or lower prior limit. For the S model, the prior ranges were set as previously [16,29].…”