A Computational Model of the Human Left-Ventricular Epicardial Myocyte

Iyer, Vivek; Mazhari, Reza; Winslow, Raimond L.

doi:10.1529/biophysj.104.043299

Cited by 261 publications

(252 citation statements)

References 70 publications

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“…[8]). SNC is found, for example, in the Iyer-Mazhari-Winslow [9] or in the Drouhard-RobergeBeeler-Reuter (DRBR) model [10,11] employed in the study here, and it is often observed under conditions of low extracellular potassium concentration [12]. Recently, it has been shown to potentiate the onset of alternans in cultured cardiac cell strands [13] and also in generic models of spiral wave formation [14].…”

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confidence: 79%

Supernormal conduction in cardiac tissue promotes concordant alternans and action potential bunching

et al. 2011

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Supernormal conduction (SNC) in excitable cardiac tissue refers to an increase of pulse (or action potential) velocity with decreasing distance to the preceding pulse. Here we employ a simple ionic model to study the effect of SNC on the propagation of action potentials (APs) and the phenomenology of alternans in excitable cardiac tissue. We use bifurcation analysis and simulations to study attraction between propagating APs caused by SNC that leads to AP pairs and bunching. It is shown that SNC stabilizes concordant alternans in arbitrarily long paced one-dimensional cables. As a consequence, spiral waves in two-dimensional tissue simulations exhibit straight nodal lines for SNC in contrast to spiraling ones in the case of normal conduction. Propagation of pulse trains in excitable media is usually characterized by a so-called dispersion curve that gives the velocity of a pulse in a periodic train as a function of its wavelength [1]. In cardiac tissue the dispersion properties are often summarized in the conduction velocity (CV) restitution curve that relates the velocity of an action potential (AP) to its diastolic interval (DI), that is, the time between two consecutive APs (see, e.g., Ref.[2]).Since a pulse typically is followed by a refractory zone of decreased excitability, the pulse velocity monotonically increases with increasing wavelength (=normal dispersion). In the context of excitable cardiac tissue, normal dispersion corresponds to normal conduction. In this paper we focus on the alternative situation where the velocity of a pulse is decreasing with increasing wavelength. In generic excitable media this case is known as anomalous dispersion. For excitable cardiac tissue it corresponds to supernormal conduction (SNC) [3]. Normal and supernormal conduction may appear for a different wavelength in the same systems, then the dispersion curve has to be nonmonotonic.The simplest case of a nonmonotonic dispersion relation is a curve with a single maximum separating anomalous dispersion at long wavelength from normal dispersion at short wavelength. Such behavior has been discovered in various experiments in chemical reaction-diffusion systems [4,5]. It has been found to coincide with attractive long-range interaction between pulses that may lead to stable bound states [6] merging or bunching of pulses [7]. While most excitable cardiac tissues and related models exhibit normal conduction, nevertheless examples of SNC are frequent (for a short review see, e.g., Ref.[8]). SNC is found, for example, in the Iyer-Mazhari-Winslow [9] or in the Drouhard-RobergeBeeler-Reuter (DRBR) model [10,11] employed in the study here, and it is often observed under conditions of low extracellular potassium concentration [12]. Recently, it has been shown to potentiate the onset of alternans in cultured cardiac cell strands [13] and also in generic models of spiral wave formation [14].Alternans is an oscillation in the duration of the AP that usually occurs at short stimulation periods [15]. In a paced cable or tissue alternans ...

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confidence: 79%

Supernormal conduction in cardiac tissue promotes concordant alternans and action potential bunching

et al. 2011

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“…Cardiac electrophysiology models often do not include tension development, but given that peak tension is reached during the decay phase of the calcium transient and that calcium buffering will have the greatest effect during peak calcium, the static unbinding rate of calcium could be approximated as ~100s -1 . This is not easily compatible with the routinely used values of 19.6s -1 (Bondarenko et al 2004, Mahajan et al 2008, Shannon et al 2000, 40s -1 (Iyer et al 2004, Pandit et al 2001) and 200s -1 (Noble et al 1998). …”

Section: Models Of Cell Contractionmentioning

confidence: 85%

“…Specifically, motivated by knowledge of protein structure, Markov models can consist of a large number of states and transitions, which may be excessive from an information theoretical point of view. For example, the transient outward K + current I to has been modelled using ten-state Markov models for each of the two components (I Kv4.3 and I Kv1.4 ), leading to a total of 20 states to describe I to (Iyer et al 2004).…”

Section: Models For Ion Channelsmentioning

confidence: 99%

Cardiac cell modelling: Observations from the heart of the cardiac physiome project

Fink

Niederer

Cherry

et al. 2011

Progress in Biophysics and Molecular Biology

149

136

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In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field.

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“…The past two decades have witnessed the development of increasingly sophisticated DEMs [9], which unravel in great detail the underlying cellular processes [17,22,24,14]. Such models are essential in the understanding of the intrinsic ionic mechanisms, and in the development of novel treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

From Cardiac Cells to Genetic Regulatory Networks

Grosu

Batt

Fenton

et al. 2011

Lecture Notes in Computer Science

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Abstract. A fundamental question in the treatment of cardiac disorders, such as tachycardia and fibrillation, is under what circumstances does such a disorder arise? To answer to this question, we develop a multiaffine hybrid automaton (MHA) cardiac-cell model, and restate the original question as one of identification of the parameter ranges under which the MHA model accurately reproduces the disorder. The MHA model is obtained from the minimal cardiac model of one of the authors (Fenton) by first bringing it into the form of a canonical, genetic regulatory network, and then linearizing its sigmoidal switches, in an optimal way. By leveraging the Rovergene tool for genetic regulatory networks, we are then able to successfully identify the parameter ranges of interest.

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A Computational Model of the Human Left-Ventricular Epicardial Myocyte

Cited by 261 publications

References 70 publications

Supernormal conduction in cardiac tissue promotes concordant alternans and action potential bunching

Supernormal conduction in cardiac tissue promotes concordant alternans and action potential bunching

Cardiac cell modelling: Observations from the heart of the cardiac physiome project

From Cardiac Cells to Genetic Regulatory Networks

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