Model Of Cardiac Action Potential Research Articles

A de novo arrhythmogenic Nav1.5 variant, E171Q, causes multiple biophysical defects.

An infant presenting antenatally with cardiac conduction disease and junctional tachycardia reminiscent of the multi-focal ectopic Purkinje-related premature contractions (MEPPC) syndrome was found to harbour the de novo E171Q variant of the cardiac voltage-gated sodium channel, Nav1.5. Functional analysis was performed using whole cell patch clamp and current clamp to compare WT Nav1.5 with E171Q expressed in HEK293 cells and iPSC-CMs. We found E171Q significantly right-shifted the voltage-dependence of both activation and steady-state fast inactivation, and increased the amplitude of late sodium current. These changes were reversed upon perfusion with 100 μM ranolazine. Incorporating the shifts in voltage-dependence and late current into the Ohara-Rudy cardiac action potential model produced a delay in action potential repolarization. Further investigation revealed a significant outward gating pore (omega) current in E171Q that was mitigated upon perfusion of 3 μM flecainide or with K+-free internal solution. Current clamp experiments in iPSC-CMs revealed action potential variability with delayed afterdepolarizations in myocytes expressing E171Q. The increase in gating pore current is consistent with disrupting the interaction between E171 and a corresponding positive charge in the S4 voltage sensor. This disruption could result in the completion of an ion-permeable pore, consistent with that previously described for Nav1.5 R222Q - a variant also associated with MEPPC - and Nav1.4 R222W.

Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation.

Customization of cardiac action potential models has become increasingly important with the recognition of patient-specific models and virtual patient cohorts as valuable predictive tools. Nevertheless, developing customized models by fitting parameters to data poses technical and methodological challenges: despite noise and variability associated with real-world datasets, traditional optimization methods produce a single "best-fit" set of parameter values. Bayesian estimation methods seek distributions of parameter values given the data by obtaining samples from the target distribution, but in practice widely known Bayesian algorithms like Markov chain Monte Carlo tend to be computationally inefficient and scale poorly with the dimensionality of parameter space. In this paper, we consider two computationally efficient Bayesian approaches: the Hamiltonian Monte Carlo (HMC) algorithm and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithm. We find that both methods successfully identify distributions of model parameters for two cardiac action potential models using model-derived synthetic data and an experimental dataset from a zebrafish heart. Although both methods appear to converge to the same distribution family and are computationally efficient, HMC generally finds narrower marginal distributions, while ABC-SMC is less sensitive to the algorithmic settings including the prior distribution.

Stochastic mixed-mode oscillations in the canards region of a cardiac action potential model

Mixed-mode oscillations (MMOs) and canards play important role in the dynamics of many mathematical models in the life sciences. In particular, pathological early afterdepolarizations in heart cells behavior can be associated with MMOs. The present paper aims to study a stochastic version of the reduced Luo–Rudy model of cardiac action potential. We show that in the parameter zone where the dynamics of the original deterministic system is characterized by canard-type limit cycles, weak additive noise can induce MMOs. Using stochastic sensitivity functions, Mahalanobis metrics, and confidence domains, we analyze the probabilistic mechanism of this stochastic phenomenon. Moreover, we find a critical value of the parameter corresponding to the “super-sensitive” cycle and show that for this value, stochastic splitting of oscillations is observed. Furthermore, we show that in the canard zone of the model, noise-driven transition to chaos occurs. To conclude, this paper reveals and analyzes new stochastic phenomena in the model of cardiac activity, moreover, the presented results can be helpful for studies of other complex models.

Combining in-silico cardiac action potentials with HERG currents expressed in HEK cells using dynamic clamp in synthetic cell mode

We used real-time dynamic clamp to allow heterologously-expressed cloned ion channel currents expressed in cells to be input into an in silico cardiac action potential (AP) model (Synthetic Cell Mode). The effect of dofetilide, a HERG blocker, on AP duration was measured. HERG currents were recorded from a stable cell line (HERG_A:HEK, BPS Bioscience) in whole cell voltage clamp. HERG currents were monitored until stable. In Synthetic Cell Mode, we input the HERG current into an in silico AP model consisting of electronically expressed INa, IK1, IKS, ICa_L and Ito.

Quantifying Distributions of Parameters for Cardiac Action Potential Models Using the Hamiltonian Monte Carlo Method.

Cardiac action potential (AP) models are typically given with a single set of parameter values; however, this approach does not consider variability and uncertainty across individuals and experimental conditions. As an alternative to single-value parameter fitting, we sought to use a Bayesian approach, the Hamiltonian Monte Carlo (HMC) algorithm, to find distributions of physiological parameter values for cardiac AP models across a range of cycle lengths (CLs) and dynamics. To assess HMC's accuracy for cardiac data, we applied it to synthetic APs from the Mitchell-Shaeffer (MS) and Fenton-Karma (FK) models with added noise over a range of physiological CLs, some of which included alternans. To show the applicability of HMC to experimental data, we calculated parameter distributions for both models using micro-electrode recordings of zebrafish APs from a range of CLs. For synthetic APs generated from three CLs using the MS (FK) models, HMC produced unimodal quasi-symmetric distributions for all five (13) parameters. APs generated by setting all parameters in the MS (FK) model to the modes of their corresponding marginal distributions yielded errors in voltage traces below 5.0% (0.6%). We also obtained distributions for the MS (FK) model parameters using zebrafish data to construct the first minimal model of the zebrafish AP, with voltage trace errors below 4.8% (3.4%). We have shown that HMC can identify not only a single set of parameter values but also viable distributions for cardiac AP model parameters using synthetic and experimental data. Because HMC generates samples from the parameter distributions based on input data, it can produce families of parameterizations that can be used in population-based modeling approaches without the need for rejecting a large number of randomly generated candidate parameterizations. HMC also has the potential to provide quantitative measures of spatial/individual variability and uncertainty.

Stochastic generation and suppression of early afterdepolarizations in a three‐dimensional model of cardiac action potential

The influence of random disturbances on a three‐dimensional simplification of Luo–Rudy model of the cardiac action potential is studied. We show that in the parameter region, where the deterministic model is in the equilibrium regime, noise can trigger large‐amplitude oscillations that correspond with pathological early afterdepolarizations (EADs). For this stochastic excitement, the phenomenon of coherence resonance was discovered. On the contrary, in another parameter zone of the model, noise can suppress EADs. We analyze these stochastic phenomena using the stochastic sensitivity functions technique, Mahalanobis distance, the methods of principal directions, and confidence domains.

Characterizing mixed-mode oscillations shaped by canard and bifurcation structure in a three-dimensional cardiac cell model

This paper investigates mixed-mode oscillations (MMOs) with a three-dimensional conductance-based cardiac action potential model, which makes the heart beat in a nonrenewable way. The 3D model was entailed by utilizing voltage-dependent timescales to describe the mechanism in which MMOs are generated. As expected, motivated by geometric singular perturbation theory, our analysis explains in detail the geometric mechanisms that there is a range of parameters under which the cardiac model highlights that the presence of MMOs is induced by the intrinsical canard phenomenon. Much is currently known about the geometric mechanisms, for a folded saddle, the two singular canards perturb to maximal canards. Characteristics of the stimulus current such as frequency and duration determine which early afterdepolarizations (EADs), as a special case of MMOs bears, as well as the article compares the detailed manifold structures of original and dimensionless systems with square wave pulses by setting the pacing cycle length. An exceedingly vital technique of the analysis is the slow–fast dynamics analysis by which the system governs multiple timescale structures analytically. A more novel and successful multiple-timescale approach divides the system so that there is only one fast variable and demonstrates that the MMOs arise from canard dynamics, such as using a three-variable model in which two variables are treated as “slow” and one treated as “fast”, which the layer problem and the reduced problem are considered to explain the trajectory on the critical manifold. Meanwhile, if one variable was regarded as the single slow variable, substantial bifurcation properties are discovered for slow–fast system, as well as general one-parameter bifurcation type is discussed for the whole system similarly. By focusing on the first Lyapunov coefficient of the Hopf bifurcation, which decides whether the bifurcation is supercritical or subcritical, it was shown that an unstable limit cycle can arise via a delayed subcritical Hopf bifurcation for the original system. Meanwhile, the dynamical studies of cardiac model have major implications for further elaborating the complex dynamic behaviors, such as EADs, which can lead to tissue-level arrhythmias. Ultimately, it has turned these researches into a considerable player in the signal and information transmission for underlying nervous systems.

Effect of constant-DI pacing on single cell pacing dynamics.

Cardiac alternans, beat-to-beat alternations in action potential duration, is a precursor to fatal arrhythmias such as ventricular fibrillation. Previous research has shown that voltage driven alternans can be suppressed by application of a constant diastolic interval (DI) pacing protocol. However, the effect of constant-DI pacing on cardiac cell dynamics and its interaction with the intracellular calcium cycle remains to be determined. Therefore, we aimed to examine the effects of constant-DI pacing on the dynamical behavior of a single-cell numerical model of cardiac action potential and the influence of voltage-calcium (V-Ca) coupling on it. Single cell dynamics were analyzed in the vicinity of the bifurcation point using a hybrid pacing protocol, a combination of constant-basic cycle length (BCL) and constant-DI pacing. We demonstrated that in a small region beneath the bifurcation point, constant-DI pacing caused the cardiac cell to remain alternans-free after switching to the constant-BCL pacing, thus introducing a region of bistability (RB). The size of the RB increased with stronger V-Ca coupling and was diminished with weaker V-Ca coupling. Overall, our findings demonstrate that the application of constant-DI pacing on cardiac cells with strong V-Ca coupling may induce permanent changes to cardiac cell dynamics increasing the utility of constant-DI pacing.

Genetic algorithm-based personalized models of human cardiac action potential.

We present a novel modification of genetic algorithm (GA) which determines personalized parameters of cardiomyocyte electrophysiology model based on set of experimental human action potential (AP) recorded at different heart rates. In order to find the steady state solution, the optimized algorithm performs simultaneous search in the parametric and slow variables spaces. We demonstrate that several GA modifications are required for effective convergence. Firstly, we used Cauchy mutation along a random direction in the parametric space. Secondly, relatively large number of elite organisms (6-10% of the population passed on to new generation) was required for effective convergence. Test runs with synthetic AP as input data indicate that algorithm error is low for high amplitude ionic currents (1.6±1.6% for IKr, 3.2±3.5% for IK1, 3.9±3.5% for INa, 8.2±6.3% for ICaL). Experimental signal-to-noise ratio above 28 dB was required for high quality GA performance. GA was validated against optical mapping recordings of human ventricular AP and mRNA expression profile of donor hearts. In particular, GA output parameters were rescaled proportionally to mRNA levels ratio between patients. We have demonstrated that mRNA-based models predict the AP waveform dependence on heart rate with high precision. The latter also provides a novel technique of model personalization that makes it possible to map gene expression profile to cardiac function.

The Fitzhugh-Nagumo System as a Model of Human Cardiac Action Potentials

The Fitzhugh-Nagumo System as a Model of Human Cardiac Action Potentials

Noise-induced early afterdepolarizations in a three-dimensional cardiac action potential model

Abstract We study the influence of noise on a mathematical model of the cardiac action potential described by a three-dimensional system of ordinary differential equations. The original deterministic system undergoes a cascade of period adding saddle-node bifurcations resulting in the appearance of small scale oscillations that correspond to early afterdepolarizations (EADs). We consider a parameter region where the deterministic system exhibits normal action potential behavior and show that a small additive noise in gating variable dynamics can induce EADs in this region. This stochastic phenomenon is confirmed by changes in the probability density distributions for phase trajectories and inter-event intervals. These qualitative changes in the system dynamics can be considered as a special stochastic P-bifurcation. The mechanism of noise-induced EADs is studied with a new semi-analytical approach based on the stochastic sensitivity function technique, the method of confidence domains and Mahalanobis metrics. Applying this approach, we give an explanation of the probabilistic mechanism of the observed stochastic phenomenon and provide the estimations of critical noise intensities for the onset of noise-induced EADs.

Restitution and adaptation measurements for the estimate of short-term cardiac action potential memory: comparison of five human ventricular models.

This computational study refines our recently published pacing protocol to measure short-term memory (STM) of cardiac action potential (AP), and apply it to five numerical models of human ventricular AP. Several formulations of electrical restitution (ER) have been provided over the years, including standard, beat-to-beat, dynamic, steady-state, which make it difficult to compare results from different studies. We discuss here the notion of dynamic ER (dER) by relating it to its steady-state counterpart, and propose a pacing protocol based on dER to measure STM under periodically varying pacing cycle length (CL). Under high and highly variable-pacing rate, all models develop STM, which can be measured over the entire sequence by means of dER. Short-term memory can also be measured on a beat-to-beat basis by estimating action potential duration (APD) adaptation after clamping CL constant. We visualize STM as a phase shift between action potential (AP) parameters over consecutive cycles of CL oscillations, and show that delay between CL and APD oscillation is nearly constant (around 92 ms) in the five models, despite variability in their intrinsic AP properties. dER, as we define it and together with other approaches described in the study, provides an univocal way to measure STM under extreme cardiac pacing conditions. Given the relevance of AP memory for repolarization dynamics and stability, STM should be considered, among other usual biomarkers, to validate and tune cardiac AP models. The possibility of extending the method to in vivo cellular and whole organ models can also be profitably explored.

Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models.

Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs—typically resulting from natural variability or measurement error—and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single “hyper-parameter” to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including “model failure”. This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty — to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.

Nonlinear dynamics of two-dimensional cardiac action potential duration mapping model with memory.

The aim of this work is the analysis of the nonlinear dynamics of two-dimensional mapping model of cardiac action potential duration (2D-map APD) with memory derived from one dimensional map (1D-map). Action potential duration (APD) restitution, which relates APD to the preceding diastolic interval (DI), is a useful tool for predicting cardiac arrhythmias. For a constant rate of stimulation the short action potential during alternans is followed by a longer DI and inversely. It has been suggested that these differences in DI are responsible for the occurrence and maintenance of APD alternans. We focus our attention on the observed bifurcations produced by a change in the stimulation period and a fixed value of a particular parameter in the model. This parameter provides new information about the dynamics of the APD with memory, such as the occurrence of bistabilities not previously described in the literature, as well as the fact that synchronization rhythms occur in different ways and in a new fashion as the stimulation frequency increases. Moreover, we show that this model is flexible enough as to accurately reflect the chaotic dynamics properties of the APD: we have highlighted the fractal structure of the strange attractor of the 2D-map APD, and we have characterized chaos by tools such as the calculation of the Lyapunov exponents, the fractal dimension and the Kolmogorov entropy, with the next objective of refining the study of the nonlinear dynamics of the duration of the action potential and to apply methods of controlling chaos.

Bond graph modelling of thecardiac action potential: implications for drift and non-unique steady states.

Mathematical models of cardiac action potentials have become increasingly important in the study of heart disease and pharmacology, but concerns linger over their robustness during long periods of simulation, in particular due to issues such as model drift and non-unique steady states. Previous studies have linked these to violation of conservation laws, but only explored those issues with respect to charge conservation in specific models. Here, we propose a general and systematic method of identifying conservation laws hidden in models of cardiac electrophysiology by using bond graphs, and develop a bond graph model of the cardiac action potential to study long-term behaviour. Bond graphs provide an explicit energy-based framework for modelling physical systems, which makes them well suited for examining conservation within electrophysiological models. We find that the charge conservation laws derived in previous studies are examples of the more general concept of a 'conserved moiety'. Conserved moieties explain model drift and non-unique steady states, generalizing the results from previous studies. The bond graph approach provides a rigorous method to check for drift and non-unique steady states in a wide range of cardiac action potential models, and can be extended to examine behaviours of other excitable systems.

Genome-wide association study of T-wave morphology

Conflicting evidence exists on the efficacy of focal impulse and rotor modulation on atrial fibrillation ablation. A potential explanation is inaccurate rotor localization from multiple rotors coexistence and a relatively large (9–11 mm) inter-electrode distance (IED) of the multi-electrode basket catheter.We studied a numerical model of cardiac action potential to reproduce one through seven rotors in a two-dimensional lattice. We estimated rotor location using phase singularity, Shannon entropy and dominant frequency. We then spatially downsampled the time series to create IEDs of 2–30 mm. The error of rotor localization was measured with reference to the dynamics of phase singularity at the original spatial resolution (IED = 1 mm). IED has a significant impact on the error using all the methods. When only one rotor is present, the error increases exponentially as a function of IED. At the clinical IED of 10 mm, the error is 3.8 mm (phase singularity), 3.7 mm (dominant frequency), and 11.8 mm (Shannon entropy). When there are more than one rotors, the error of rotor localization increases 10-fold. The error based on the phase singularity method at the clinical IED of 10 mm ranges from 30.0 mm (two rotors) to 96.1 mm (five rotors).The magnitude of error of rotor localization using a clinically available basket catheter, in the presence of multiple rotors might be high enough to impact the accuracy of targeting during AF ablation. Improvement of catheter design and development of high-density mapping catheters may improve clinical outcomes of FIRM-guided AF ablation.

Simultaneous Quantification of Spatially Discordant Alternans in Voltage and Intracellular Calcium in Langendorff-Perfused Rabbit Hearts and Inconsistencies with Models of Cardiac Action Potentials and Ca Transients.

Rationale: Discordant alternans, a phenomenon in which the action potential duration (APDs) and/or intracellular calcium transient durations (CaDs) in different spatial regions of cardiac tissue are out of phase, present a dynamical instability for complex spatial dispersion that can be associated with long-QT syndrome (LQTS) and the initiation of reentrant arrhythmias. Because the use of numerical simulations to investigate arrhythmic effects, such as acquired LQTS by drugs is beginning to be studied by the FDA, it is crucial to validate mathematical models that may be used during this process.Objective: In this study, we characterized with high spatio-temporal resolution the development of discordant alternans patterns in transmembrane voltage (Vm) and intracellular calcium concentration ([Cai]+2) as a function of pacing period in rabbit hearts. Then we compared the dynamics to that of the latest state-of-the-art model for ventricular action potentials and calcium transients to better understand the underlying mechanisms of discordant alternans and compared the experimental data to the mathematical models representing Vm and [Cai]+2 dynamics.Methods and Results: We performed simultaneous dual optical mapping imaging of Vm and [Cai]+2 in Langendorff-perfused rabbit hearts with higher spatial resolutions compared with previous studies. The rabbit hearts developed discordant alternans through decreased pacing period protocols and we quantified the presence of multiple nodal points along the direction of wave propagation, both in APD and CaD, and compared these findings with results from theoretical models. In experiments, the nodal lines of CaD alternans have a steeper slope than those of APD alternans, but not as steep as predicted by numerical simulations in rabbit models. We further quantified several additional discrepancies between models and experiments.Conclusions: Alternans in CaD have nodal lines that are about an order of magnitude steeper compared to those of APD alternans. Current action potential models lack the necessary coupling between voltage and calcium compared to experiments and fail to reproduce some key dynamics such as, voltage amplitude alternans, smooth development of calcium alternans in time, conduction velocity and the steepness of the nodal lines of APD and CaD.

Impact of number of co-existing rotors and inter-electrode distance on accuracy of rotor localization

BackgroundConflicting evidence exists on the efficacy of focal impulse and rotor modulation on atrial fibrillation ablation. A potential explanation is inaccurate rotor localization from multiple rotors coexistence and a relatively large (9–11mm) inter-electrode distance (IED) of the multi-electrode basket catheter. Methods and resultsWe studied a numerical model of cardiac action potential to reproduce one through seven rotors in a two-dimensional lattice. We estimated rotor location using phase singularity, Shannon entropy and dominant frequency. We then spatially downsampled the time series to create IEDs of 2–30mm. The error of rotor localization was measured with reference to the dynamics of phase singularity at the original spatial resolution (IED=1mm). IED has a significant impact on the error using all the methods. When only one rotor is present, the error increases exponentially as a function of IED. At the clinical IED of 10mm, the error is 3.8mm (phase singularity), 3.7mm (dominant frequency), and 11.8mm (Shannon entropy). When there are more than one rotors, the error of rotor localization increases 10-fold. The error based on the phase singularity method at the clinical IED of 10mm ranges from 30.0mm (two rotors) to 96.1mm (five rotors). ConclusionsThe magnitude of error of rotor localization using a clinically available basket catheter, in the presence of multiple rotors might be high enough to impact the accuracy of targeting during AF ablation. Improvement of catheter design and development of high-density mapping catheters may improve clinical outcomes of FIRM-guided AF ablation.

Efficient parameterization of cardiac action potential models using a genetic algorithm.

Finding appropriate values for parameters in mathematical models of cardiac cells is a challenging task. Here, we show that it is possible to obtain good parameterizations in as little as 30-40 s when as many as 27 parameters are fit simultaneously using a genetic algorithm and two flexible phenomenological models of cardiac action potentials. We demonstrate how our implementation works by considering cases of "model recovery" in which we attempt to find parameter values that match model-derived action potential data from several cycle lengths. We assess performance by evaluating the parameter values obtained, action potentials at fit and non-fit cycle lengths, and bifurcation plots for fidelity to the truth as well as consistency across different runs of the algorithm. We also fit the models to action potentials recorded experimentally using microelectrodes and analyze performance. We find that our implementation can efficiently obtain model parameterizations that are in good agreement with the dynamics exhibited by the underlying systems that are included in the fitting process. However, the parameter values obtained in good parameterizations can exhibit a significant amount of variability, raising issues of parameter identifiability and sensitivity. Along similar lines, we also find that the two models differ in terms of the ease of obtaining parameterizations that reproduce model dynamics accurately, most likely reflecting different levels of parameter identifiability for the two models.

Nonlinear diffusion and thermo-electric coupling in a two-variable model of cardiac action potential

This work reports the results of the theoretical investigation of nonlinear dynamics and spiral wave breakup in a generalized two-variable model of cardiac action potential accounting for thermo-electric coupling and diffusion nonlinearities. As customary in excitable media, the common Q10 and Moore factors are used to describe thermo-electric feedback in a 10° range. Motivated by the porous nature of the cardiac tissue, in this study we also propose a nonlinear Fickian flux formulated by Taylor expanding the voltage dependent diffusion coefficient up to quadratic terms. A fine tuning of the diffusive parameters is performed a priori to match the conduction velocity of the equivalent cable model. The resulting combined effects are then studied by numerically simulating different stimulation protocols on a one-dimensional cable. Model features are compared in terms of action potential morphology, restitution curves, frequency spectra, and spatio-temporal phase differences. Two-dimensional long-run simulations are finally performed to characterize spiral breakup during sustained fibrillation at different thermal states. Temperature and nonlinear diffusion effects are found to impact the repolarization phase of the action potential wave with non-monotone patterns and to increase the propensity of arrhythmogenesis.