Limitations in electrophysiological model development and validation caused by differences between simulations and experimental protocols

Abstract

Models of ion channel dynamics are usually built by fitting isolated cell experimental values of individual parameters while neglecting the interaction between them. Another shortcoming regards the estimation of ionic current conductances, which is often based on quantification of Action Potential (AP)-derived markers. Although this procedure reduces the uncertainty in the calculation of conductances, many studies evaluate electrophysiological AP-derived markers from single cell simulations, whereas experimental measurements are obtained from tissue preparations. In this work, we explore the limitations of these approaches to estimate ion channel dynamics and maximum current conductances and how they could be overcome by using multiscale simulations of experimental protocols. Four human ventricular cell models, namely ten Tusscher and Panfilov (2006), Grandi et al. (2010), O'Hara et al. (2011), and Carro et al. (2011), were used. Two problems involving scales from ion channels to tissue were investigated: 1) characterization of L-type calcium voltage-dependent inactivation I; 2) identification of major ionic conductance contributors to steady-state AP markers, including APD, APD, APD, APD, Triangulation and maximal and minimal values of V and dV/dt during the AP (V, V, dV/dt, dV/dt). Our results show that: 1) I inactivation characteristics differed significantly when calculated from model equations and from simulations reproducing the experimental protocols. 2) Large differences were found in the ionic currents contributors to APD, Triangulation, V, dV/dt and dV/dt between single cells and 1D-tissue. When proposing any new model formulation, or evaluating an existing model, consistency between simulated and experimental data should be verified considering all involved effects and scales.

Data-Driven Identification of Stochastic Model Parameters and State Variables: Application to the Study of Cardiac Beat-to-Beat Variability

“…The third term, k(t), represents the projection that serves to ensure that s(t) remains in the probability simplex [37], as described in [35]. Stochasticity was included in the ionic gating of four different currents, namely I Ks , I Kr , I to and I CaL , which are major currents active during AP repolarization [4], [25], [38]. Following the derivations of [4], [35], matrices A, E (stoichiometry) and D (containing the rate of each transition as a function of s) in equation (1) for the stochastic ORd model were calculated as described in the following.…”

A response surface optimization approach to adjust ionic current conductances of cardiac electrophysiological models. Application to the study of potassium level changes

“…One of the differences between the GPB / CRLP models and the other human ventricular models is the inclusion of the I Cl , bk current. This current was expected to have a minor role in modulating electrophysiological properties, but, as reported in [ 33 ], APD showed the highest sensitivity (-86%) to changes in the maximal ionic conductance of the I Cl , bk current. In the CRLP model, this sensitivity was reduced (-46%) but it was still large.…”

“…The range for triangulation was 44-112 ms [ 31 , 32 ]. For the diastolic and systolic [ Ca 2+ ] i levels at 1 Hz and 0.5 Hz stimulation, the CRLP model, as occurs with other recent human ventricular AP models (GPB, ORd, TP06, TP04) [ 4 , 5 , 7 , 16 , 17 , 33 , 34 ], is out of the physiological range reported in the literature [ 35 , 36 ]. For that reason, the physiological range was extended to ensure a feasible solution of the optimization problem.…”

Heart Rhythm Insights Into Structural Remodeling in Atrial Tissue: Timed Automata Approach

“…The initial model has been enriched in many ways, by adding a much larger number of ion currents and also by including other mechanisms of cellular electrophysiology such as active ions pumps, intercellular compartments for calcium transport, and calcium buffers, see http://www.physiome.org/jsim/models/webmodel/NSR/Luo-Rudy/ or other projects of CellML (Miller et al, 2010). In consequence, the most detailed Luo-Rudy model of the human ventricular myocyte, known now as O'Hara-Rudy (O'Hara et al, 2011), involves 41 state variables in more than 100 differential-algebraic equations; see the latest review of electrophysiological models of the ventricular cell in Carro et al (2017).…”