Period doubling cascades of limit cycles in cardiac action potential models as precursors to chaotic early Afterdepolarizations
 Philipp Kügler^{1, 2}Email author,
 M.A.K. Bulelzai^{2} and
 André H. Erhardt^{1}
DOI: 10.1186/s1291801704224
© The Author(s) 2017
Received: 6 October 2016
Accepted: 24 March 2017
Published: 4 April 2017
Abstract
Background
Early afterdepolarizations (EADs) are pathological voltage oscillations during the repolarization phase of cardiac action potentials (APs). EADs are caused by drugs, oxidative stress or ion channel disease, and they are considered as potential precursors to cardiac arrhythmias in recent attempts to redefine the cardiac drug safety paradigm. The irregular behaviour of EADs observed in experiments has been previously attributed to chaotic EAD dynamics under periodic pacing, made possible by a homoclinic bifurcation in the fast subsystem of the deterministic AP system of differential equations.
Results
In this article we demonstrate that a homoclinic bifurcation in the fast subsystem of the action potential model is neither a necessary nor a sufficient condition for the genesis of chaotic EADs. We rather argue that a cascade of period doubling (PD) bifurcations of limit cycles in the full AP system paves the way to chaotic EAD dynamics across a variety of models including a) periodically paced and spontaneously active cardiomyocytes, b) periodically paced and nonactive cardiomyocytes as well as c) unpaced and spontaneously active cardiomyocytes. Furthermore, our bifurcation analysis reveals that chaotic EAD dynamics may coexist in a stable manner with fully regular AP dynamics, where only the initial conditions decide which type of dynamics is displayed.
Conclusions
EADs are a potential source of cardiac arrhythmias and hence are of relevance both from the viewpoint of drug cardiotoxicity testing and the treatment of cardiomyopathies. The modelindependent association of chaotic EADs with period doubling cascades of limit cycles introduced in this article opens novel opportunities to study chaotic EADs by means of bifurcation control theory and inverse bifurcation analysis. Furthermore, our results may shed new light on the synchronization and propagation of chaotic EADs in homogeneous and heterogeneous multicellular and cardiac tissue preparations.
Keywords
Nonlinear dynamics Chaos Bifurcation theory Cardiac action potential Early afterdepolarizationsBackground
derived from the experimentally obtained restitution curve (APD plotted against the diastolic interval (DI)), it has been shown that, as the PCL of the stimulus is decreased, the slope of F at the fixed point APD^{∗} of Eq. (1) becomes progressively steeper until ∂ F(APD^{∗},PCL_{crit})/∂APD=−1 at some critical value PCL_{crit}. At this flip bifurcation point PCL_{crit} the stability of the fixed point is lost and a period2 cycle of Eq. (1) is born. The latter marks the beginning of a cascade of perioddoubling bifurcations, also compare with [4], that is produced as PCL is further decreased and that finally leads to chaotic sequences of APD generated by Eq. (1). This is in accordance with the PD route to chaos in the general theory of iterated maps [1]. For later reference we emphasize that [2] studied chaotic APD variations in periodically forced cardiac pacemakers cells (i.e., they show spontaneous action potential oscillations in the absence of a stimulus), while [3] studied chaotic APD variations in periodically forced nonspontaneously active Purkinje fibre and ventricular muscle cells.
Finally, it has been argued in [5] that the steep positive slope of the restitution curve r before the peak (which translates into a steep negative slope of the map F at the fixed point APD ^{∗}) proves the chaotic nature of the EAD dynamics.
In this paper, we demonstrate that a steep slope of the restitution curve r cannot serve as a general explanation of chaotic EAD dynamics displayed by cardiac AP models. Indeed, we will show that chaotic EAD dynamics in Eq. (2) are possible even if extracted ADP and DI data points do not form a function r (hence, not even admitting to speak of a slope). However, the key contribution of our paper will be novel insight into chaotic EAD dynamics gained from mathematical bifurcation studies of differential equation models of the form Eq. (2). Using a separation into fast and slow time scale variables, bifurcation analysis has been previously applied in [10] for the illumination of EADs in (a variant of) the periodically driven LR91model [11] for ventricular cardiomyocytes. In particular, it was shown in [10] that the fast subsystem of Eq. (2) features a supercritical Hopf bifurcation from which stable limit cycles emerge until they terminate at a homoclinic bifurcation of a saddle equilibrium. Then, EAD behaviour is obtained if the model parameters are set such that the state trajectory of the full system Eq. (2) temporarily coils around the limit cycle surface spanned between the supercritical Hopf and the homoclinic bifurcations of the fast subsystem. Furthermore, the homoclinic bifurcation in the fast subsystem has been introduced in [10] as the reason for the chaotic EAD dynamics that could be obtained whenever the PCL was chosen appropriately in an EAD featuring parameter setting. Further affirmations of the statement that chaotic EAD dynamics in periodically triggered action potentials are due to a homoclinic bifurcation in the fast subsystem of Eq. (2) are given in [12–15].
Recently, we have shown in [16] that EADs may occur in action potential models Eq. (2) that do not feature a supercritical Hopf bifurcation in their fast subsystem. In this paper, we now demonstrate that a homoclinic bifurcation in the fast subsystem of Eq. (2) is neither a necessary nor a sufficient condition for obtaining chaotic EAD dynamics.
We rather argue that a PD cascade of limit cycles in the full action potential system Eq. (2) paves the way to chaotic EAD dynamics in a modelindependent manner and present examples with models of a) periodically paced and spontaneously active cardiomyocytes, b) periodically paced and nonactive cardiomyocytes as well as c) unpaced and spontaneously active cardiomyocytes. Furthermore, we reveal that chaotic EAD dynamics may coexist in a stable manner with regular action potential dynamics, where only the initial conditions decide about the dynamics displayed. The results of our article on chaotic EAD dynamics in single cardiomyocytes may shed new light on the synchronization of chaotic EADs [5, 17] and EADmediated fibrillation [18] in cardiac tissue, may open new paths for the control of cardiac chaos [19] and may be of relevance within the CiPAinitiative [20] for a new approach to preclinical drug cardiotoxicity testing with hiPSCCMs, which considers EADs as a potential mechanismbased metric for the assessment of proarrhythmic risk.
Methods
Cardiac action potential models
Modelling of cardiac action potentials with systems of nonlinear ordinary differential equations (ODEs) (in which the voltage Eq. (2) is coupled to differential equations that describe the dynamics of the ion channel currents I _{ ion }) dates back to the work of Denis Noble [21] on both Purkinje fibre and pacemaker cells. Modern cardiac AP models for animal [22], human adult [23] and human induced pluripotent stem cell derived [24] cardiomyocytes comprise dozens of state variables and hundreds of model parameters. Typically, the only explicit timedependence of cardiac AP models is due to the stimulating current I _{ sti } such that without stimulation, i.e., I _{ sti }≡0, the AP model forms an autonomous ODE system. Depending on the actual equations and the particular choice of the model parameters, the autonomous ODE system may show limit cycle behaviour (then modelling spontaneous beating activity such as in pacemaker cells) or steady state behaviour (then modelling cells that show no spontaneous AP activity such as ventricular cardiomyocytes). Using inverse bifurcation analysis with sparsity promoting penalization [25, 26], models for spontaneously beating cells can be transferred into models for nonactive cells and vice versa. Anyhow, the application of a periodic stimulus I _{ sti } with pacing cycle length PCL leads to AP models of either periodically paced and spontaneously active or periodically paced and spontaneously nonactive cells. In our study we used the following representatives for each of the three available classes of AP models. If the values of the model parameters are not explicitly mentioned in the text, they were chosen to be identical to those used in the original publications.
AP Model PP –Periodically paced pacemaker cell
AP Model PV – Periodically paced ventricular cell
as also considered in [10].
AP Model UP – Unpaced pacemaker cell
As an example of an AP model of unforced and spontaneously active cells we chose Eq. (4) with the setting τ _{ f }=18, τ _{ x }=100 (from [27], see also [16]) and I _{ sti }≡0, and the initial conditions y _{0}=(V _{0},f _{0},x _{0})=(−75.16,0.9984,0.03976).
Numerical simulation of action potential models
For the numerical simulation of the AP models PP (paced pacemaker), PV (paced ventricular) and UP (unpaced pacemaker) we used the MATLAB [28] solver ode15s for stiff ODE systems with the relative tolerance option set to 10^{−12}. All models were simulated for a time span of 2000 s. Then, the first 500 s were discarded in order to eliminate possible transient effects, such that only the remaining 1500 s of the simulations were used for the analysis.
Numerical bifurcation analysis of AP models
Here, A and d denote the amplitude and the duration of the step pulse, see the Additional file 1 for further details.
The stable branches of the bifurcation diagrams were crosschecked and partially complemented (in case of a numerical continuation failure) by a parametric sweep, i.e., by numerical simulations of Eq. (2) that were repeated for a variety of different parameter values.
Multiple time scale analysis of AP models
was derived in which x acts as a model parameter. Finally, a bifurcation analysis of Eq. (9) for the setting α=0.1 and β=1.1 and with x as the continuation parameter revealed that Eq. (9) features a supercritical Hopf bifurcation followed by a homoclinic bifurcation to a saddle fixed point. While the Hopf bifurcation was considered in [10] to be necessary for the genesis of EADs in the full system Eq. (5), the homoclinic bifurcation was introduced as the reason for the occurence of chaotic EAD dynamics in Eq. (5) under an appropriate periodic stimulation.
of the cardiac AP models PP and UP, then again with the slow variable x as the bifurcation parameter. The submodel Eq. (10) was previously used in [16] in order to demonstrate that a supercritical Hopf bifurcation in a fast subsystem is actually not necessary for EAD genesis.
Calculation of Lyapunov exponents
A positive value of λ corresponds with an exponentially fast growth of the initial perturbation δ _{0} and means that the respective trajectory is chaotic.
Calculation of restitution curves
As considered in [5] and [12] as an alternative to the S1S2protocol, we derived the restitution curves directly from the simulated voltage traces. To this end, all time points at which the voltage crosses the threshold value V _{ th } were recorded during the simulations using the MATLAB event option of the ODE solver. Action potential durations APDs were then calculated as the time spans during which the voltage lies above V _{ th }, while diastolic intervals DIs were constructed as the time spans with voltage values below V _{ th }. Finally, pairs of DI and subsequent APD were built and plotted as APD _{ n+1} vs. DI _{ n }. Our motivation for the use of this direct method was its straightforward applicability to both paced and spontaneaously active models without the need of introducing perturbations to the latter. For the sake of completeness, we also applied the S1S2protocol, see the Additional file 2.
Results
Simulations of chaotic EADs via the numerical integration of Eq. (2) have so far only been reported [5, 10, 12] for the case of a periodic stimulation by an external current I _{ sti }, see Figs. 1PP and 1PV for examples. However, studying drug induced EADs in spontaneously beating human induced pluripotent stem cell derived cardiomyocytes, we found that chaotic EADs may also form in simulations of AP models without periodic stimulation, see Fig. 1UP. Note that a comparable high number of small oscillations during EADlike activity has been experimentally observed in [36]. As the occurence of chaotic EADs of the type shown in Fig. 1PP (i.e., the AP is only triggered by the external current after full repolarization) has been attributed in [5, 12] to the steep slope of the APD restitution curve, see Fig. 2PP, we first wondered if this argument may also apply to chaotic EADs of the types shown in Figs. 1PV (external stimulation also before full repolarization) and 1UP (no external stimulation at all). Having derived the corresponding APD restitution curves, see Figs. 2PV and 2UP, we realized that they strongly deviate from their previously published counterparts as exemplified in Fig. 2PP. In particular, due to the lack of continuity and differentiability properties the “APD restitution curves” of Figs. 2PV and 2UP do not allow to define maps Eq. (1) for the iteration of APD and hence do not contribute to the understanding of the chaotic EAD types shown in Figs. 1PV and 1UP.
Having associated the chaotic EAD dynamics of the model UP with the perioddoubling route to chaos in ODE systems [30], we wondered if this phenomenon, numerically and experimentally observed in many other biological and physical systems including neuronal activity [37, 38], may also underlie the chaotic EAD dynamics in the nonautonomous models PP and PV. Transforming the latter into autonomous systems using Eqs. (7) and (8), we hence performed a numerical bifurcation analysis again with G _{ K } as the continuation parameter. The resulting bifurcation diagrams, displayed in Figs. 4PP and 4PV, reveal that also in the case of model PP and PV, the value of G _{ K } corresponding to the chaotic EAD dynamics shown in Fig. 1 in fact lies in close vicinity to a cascade of period doubling bifurcations. For illustrations of the corresponding periodic trajectories we refer to the Additional file 4.
Discussion
Chaotic EAD dynamics in AP models Eq. (2) have been previously observed in the case of periodic pacing [5, 10, 12] but have been attributed to either the steepness of APD restitution curves or the existence of homoclinic bifurcations in fast AP subdynamics. While none of these results is able to explain the chaotic EAD dynamics observed in the unforced AP model UP, our study suggests the existence of a cascade of period doubling bifurcations of limit cycles as a modelindependent explanation for chaotic EAD dynamics both in forced and unforced cardiac AP systems of the ODE type. We emphasize that period doubling bifurcations, though then observed in iterated APD maps Eq. (1) rather than in mechanistic cardiac AP models Eq. (2), have so far only been linked with chaotic APD alternans [2–4] (which constitutes a type of cardiac arrhythmia that is different from EADs).
The results of this study were obtained by means of bifurcation analysis applied to AP models Eq. (2) based on numerical continuation using the software packages matcont [31] and auto07p [32]. In the context of EADsresearch, numerical continuation was previously only applied to fast subsystems of Eq. (2), see [10, 16]. Though the analysis of fast subsystems can illuminate EAD generating mechanisms during a single AP [10, 16], its capability to study the occurence of EADs over a time span covering several APs may be limited as the conditions for a separation into fast and slow state variables [34] may not be met in the long term. In that regard, the proper consideration of the stimulating current in Eq. (9) or Eq. (10), which certainly constitutes a very fast component of the system, might be one hurdle to be taken. In contrast, the advantage of studying the dynamical behaviour by a bifurcation analysis of the full AP model Eq. (2) is that its long term behaviour defined by stable and chaotic attractors can be captured and tracked as model parameters are continuously varied. Besides of the detection of bifurcations such as Hopf, saddle node of cycles, homoclinic or period doubling bifurcations, another benefit of bifurcation analysis with numerical continuation is that unstable (i.e., nonattracting) dynamical structures of Eq. (2) can be revealed. While the interpretation of the unstable limit cycles illustrated in Fig. 4 goes beyond the scope of this study, the relevance of unstable structures in the context of AP modelling is highlighted in [39], in which the existence of an unstable chaotic invariant set suggests that the excitability of a membrane to fire an AP may be more complex than a smooth hypersurface that divides subthreshold and suprathreshold membrane potentials.
where \(G_{K_{n}}\) is the parameter value of G _{ K } corresponding to the nperiod doubling PD _{ n }.
This is similar to the coexistence of periodic spiking and chaotic bursting reported for a nonforced spontaneously active neuron model in [40], then attributed to a bifurcation of a saddlenode periodic orbit. Hence, even though the fast subsystem of model PV features a homoclinic bifurcation, the latter is not sufficient for the occurence of chaotic EAD dynamics as in addition to the model parameters also the initial conditions need to be properly set. This coexistence is of relevance both for numerical and experimental studies of cardiac action potentials which typically are based on the assumption that after a sufficiently long transient the (one and only) “steady state” periodic behaviour is observed independently of the pacing history.
We end this section with some limitations of our study. Our study focuses on the generation of chaotic EADs in ODE models Eq. (2) that represent the behaviour of single cardiomyocytes. Clearly, our work needs to be extended to PDE models of cardioelectrophysiology in order to take into account the coupling between cells and spatial AP wave propagation. Furthermore, we have not incorporated stochastic effects which also may have an impact on the bifurcation repertoire of dynamical systems. Finally, the discontinuities in the bifurcation diagrams due to a failure of the numerical continuation and the unraveled unstable limit cycle branches require further analysis to extract the full information about the dynamical repertoire of Eq. (2). Note, however, that our bifurcation approach led to the discovery of chaotic EADs in unforced cardiac AP models and furthermore offers an explanation via the PD route to chaos then also uniformly applicable to chaotic EADs in forced cardiac AP models.
Conclusions
Abbreviations
 AP:

Action potential
 APD:

Action potential duration
 CiPA:

Comprehensive Invitro proarrhythmic assay
 DI:

Diastolic interval
 EAD:

Eearly afterdepolarization
 hiPSCCM:

Human induced pluripotent stem cell derived cardiomyocyte
 ODE:

Ordinary differential equation
 PD:

Period doubling
 PCL:

Pacing cycle length
 PP:

Paced pacemaker
 PV:

Paced ventricular
 UP:

Unpaced pacemaker
Declarations
Acknowledgements
Not applicable.
Funding
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Availability of data and materials
All information necessary for reproducing the computational results is given in the article and its additional files. The manuscript does not involve data or material of other type.
Authors contributions
PK designed the study and wrote the manuscript. PK, MB and AE conducted research and discussed the manuscript. All authors read and approved the manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
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Ethics approval and consent to participate
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Authors’ Affiliations
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