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Voltage‐gated sodium channels (NavChs) are biological pores that control the flow of sodium ions through the cell membrane. In humans, mutations in genes encoding NavChs can disrupt physiological cellular activity thus leading to a wide spectrum of diseases. Here, we present a topological connection between the functional architecture of a NavAb bacterial channel and accumulation of atomic hydropathicity around its pore. This connection is established via a scaling analysis methodology that elucidates how intrachannel hydropathic density variations translate into hydropathic dipole field configurations along the pore. Our findings suggest the existence of a nonrandom cumulative hydropathic topology that is organized parallel to the membrane surface so that pore's stability, as well as, gating behavior are guaranteed. Given the biophysical significance of the hydropathic effect, our study seeks to provide a computational framework for studying cumulative hydropathic topological properties of NavChs and pore‐forming proteins in general. [ABSTRACT FROM AUTHOR]

Background: Gain-of-function mutations in SCN9A gene that encodes the voltage-gated sodium channel NaV1.7 have been associated with a wide spEctrum of painful syndromes in humans including inherited erythromelalgia, paroxysmal extreme pain disorder and small fibre neuropathy. These mutations change the biophysical properties of NaV1.7 channels leading to hyperexcitability of dorsal root ganglion nociceptors and pain symptoms. There is a need for better understanding of how gain-of-function mutations alter the atomic structure of Nav1.7. Results: We used homology modeling to build an atomic model of NaV1.7 and a network-based theoretical approach, which can predict interatomic interactions and connEctivity arrangements, to investigate how pain-related NaV1.7 mutations may alter specific interatomic bonds and cause connEctivity rearrangement, compared to benign variants and polymorphisms. For each amino acid substitution, we calculated the topological parameters betweenness centrality (Bct), degree (D), clustering coefficient (CCct), closeness (Cct), and eccentricity (Ect), and calculated their variation (Δvalue=mutant value-WT value). Pathogenic NaV1.7 mutations showed significantly higher variation of ∣ΔBct∣ compared to benign variants and polymorphisms. Using the cut-off value ±0.26 calculated by receiver operating curve analysis, we found that ΔBct corrEctly differentiated pathogenic NaV1.7 mutations from variants not causing biophysical abnormalities (nABN) and homologous SNPs (hSNPs) with 76% sensitivity and 83% specificity. Conclusions: Our in-silico analyses predict that pain-related pathogenic NaV1.7 mutations may affEct the network topological properties of the protein and suggest ∣ΔBct∣ value as a potential in-silico marker. [ABSTRACT FROM AUTHOR]

Beat-to-beat variability of repolarization duration (BVR) is an intrinsic characteristic of cardiac function and a better marker of proarrhythmia than repolarization prolongation alone. The ionic mechanisms underlying baseline BVR in physiological conditions, its rate dependence, and the factors contributing to increased BVR in pathologies remain incompletely understood. Here, we employed computer modeling to provide novel insights into the subcellular mechanisms of BVR under physiological conditions and during simulated drug-induced repolarization prolongation, mimicking long-QT syndromes type 1, 2, and 3. We developed stochastic implementations of 13 major ionic currents and fluxes in a model of canine ventricular-myocyte electrophysiology. Combined stochastic gating of these components resulted in short- and long-term variability, consistent with experimental data from isolated canine ventricular myocytes. The model indicated that the magnitude of stochastic fluctuations is rate dependent due to the rate dependence of action-potential (AP) duration (APD). This process (the “active” component) and the intrinsic nonlinear relationship between membrane current and APD (“intrinsic component”) contribute to the rate dependence of BVR. We identified a major role in physiological BVR for stochastic gating of the persistent Na+ current (INa) and rapidly activating delayed-rectifier K+ current (IKr). Inhibition of IKr or augmentation of INa significantly increased BVR, whereas subsequent β-adrenergic receptor stimulation reduced it, similar to experimental findings in isolated myocytes. In contrast, β-adrenergic stimulation increased BVR in simulated long-QT syndrome type 1. In addition to stochastic channel gating, AP morphology, APD, and beat-to-beat variations in Ca2+ were found to modulate single-cell BVR. Cell-to-cell coupling decreased BVR and this was more pronounced when a model cell with increased BVR was coupled to a model cell with normal BVR. In conclusion, our results provide new insights into the ionic mechanisms underlying BVR and suggest that BVR reflects multiple potentially proarrhythmic parameters, including increased ion-channel stochasticity, prolonged APD, and abnormal Ca2+ handling. [ABSTRACT FROM AUTHOR]

In this paper we study the potential of gene–protein interaction networks to store input–output patterns. The central question in this study concerns the memory capacity of a network of a given number of genes and proteins, which interact according to a linear state space model with external inputs. Here it is assumed that to a certain combination of inputs there exists an optimal state of the system, i.e., values of the gene expressions and protein levels, that has been attained externally, e.g., through evolutionary learning. Given such a set of learned optimal input–output patterns, the design question here is to find a sparse and hierarchical network structure for the gene–protein interactions and the gene-input couplings. This problem is formulated as an optimization problem in a linear programming setting. Numerical analysis shows that there are clear scale-invariant continuous second-order phase transitions for the network sparsity as the number of patterns increases. These phase transitions divide the system in three regions with different memory characteristics. It is possible to formulate simple scaling rules for the behavior of the network sparsity. Finally, numerical experiments show that these patterns are stable within a certain finite range around the patterns. [ABSTRACT FROM AUTHOR]

Ion channels exhibit a remarkably high accuracy in selecting uniquely its associated type of ion. The mechanisms behind ion selectivity are not well understood. Current explanations build mainly on molecular biology and bioinformatics. Here we propose a simple physical model for ion selectivity based on the driven damped harmonic oscillator (DDHO). The driving force for this oscillator is provided by self-organizing harmonic turbulent structures in the dehydrating ionic flow through the ion channel, namely, oscillating pressure waves in one dimension, and toroidal vortices in two and three dimensions. Density fluctuations caused by these turbulences efficiently transmit their energy to aqua ions that resonate with the driving frequency. Consequently, these release their hydration shell and exit the ion channel as free ions. Existing modeling frameworks do not express the required complex spatiotemporal dynamics, hence we introduce a macroscopic continuum model for ionic dehydration and transport, based on the hydrodynamics of a dissipative ionic flow through an ion channel, subject to electrostatic and amphiphilic interactions. This model combines three classical physical fields: Navier-Stokes equations from hydrodynamics, Gauss's law from Maxwell theory, and the convection-diffusion equation from continuum physics. Numerical experiments with mixtures of chemical species of ions in various degrees of hydration indeed reveal the emergence of strong oscillations in the ionic flow that are instrumental in the efficient dehydration and cause a strong ionic jet into the cell. As such, they provide a powerful engine for the DDHO mechanism. Theoretical predictions of the modeling framework match significantly with empirical patch-clamp data. The DDHO standard response curve defines a unique resonance frequency that depends on the mass and charge of the ion. In this way, the driving oscillations act as a selection mechanism that filters out one specific ion. Application of the DDHO model to real ion data shows that this mechanism indeed clearly distinguishes between chemical species and between aqua and bare ions with a large Mahalanobis distance and high oscillator quality. The DDHO framework helps to understand how SNP mutations can cause severe genetic pathologies as they destroy the geometry of the channel and so alter the resonance peaks of the required ion type. [ABSTRACT FROM AUTHOR]

Abstract: Local signaling domains and numerous interacting molecular pathways and substrates contribute to the whole-cell response of myocytes during β-adrenergic stimulation (βARS). We aimed to elucidate the quantitative contribution of substrates and their local signaling environments during βARS to the canine epicardial ventricular myocyte electrophysiology and calcium transient (CaT). We present a computational compartmental model of βARS and its electrophysiological effects. Novel aspects of the model include localized signaling domains, incorporation of β1 and β2 receptor isoforms, a detailed population-based approach to integrate the βAR and Ca2+/Calmodulin kinase (CaMKII) signaling pathways and their effects on a wide range of substrates that affect whole-cell electrophysiology and CaT. The model identifies major roles for phosphodiesterases, adenylyl cyclases, PKA and restricted diffusion in the control of local cAMP levels and shows that activation of specific cAMP domains by different receptor isoforms allows for specific control of action potential and CaT properties. In addition, the model predicts increased CaMKII activity during βARS due to rate-dependent accumulation and increased Ca2+ cycling. CaMKII inhibition, reduced compartmentation, and selective blockade of β1AR is predicted to reduce the occurrence of delayed afterdepolarizations during βARS. Finally, the relative contribution of each PKA substrate to whole-cell electrophysiology is quantified by comparing simulations with and without phosphorylation of each target. In conclusion, this model enhances our understanding of localized βAR signaling and its whole-cell effects in ventricular myocytes by incorporating receptor isoforms, multiple pathways and a detailed representation of multiple-target phosphorylation; it provides a basis for further studies of βARS under pathological conditions. [Copyright &y& Elsevier]

Noninvasive, detailed assessment of electrical cardiac activity at the level of the heart surface has the potential to revolutionize diagnostics and therapy of cardiac pathologies. Due to the requirement of noninvasiveness, body-surface potentials are measured and have to be projected back to the heart surface, yielding an ill-posed inverse problem. Ill-posedness ensures that there are non-unique solutions to this problem, resulting in a problem of choice. In the current paper, it is proposed to restrict this choice by requiring that the time series of reconstructed heart-surface potentials is sparse in the wavelet domain. A local search technique is introduced that pursues a sparse solution, using an orthogonal wavelet transform. Epicardial potentials reconstructed from this method are compared to those from existing methods, and validated with actual intracardiac recordings. The new technique improves the reconstructions in terms of smoothness and recovers physiologically meaningful details. Additionally, reconstruction of activation timing seems to be improved when pursuing sparsity of the reconstructed signals in the wavelet domain.

The inverse problem of electrocardiography is to noninvasively reconstruct electrical heart activity from body-surface electrocardiograms. Solving this problem is beneficial to clinical practice. However, reconstructions cannot be obtained straightforwardly due to the ill-posed nature of this problem. Therefore, regularization schemes are necessary to arrive at realistic solutions. To date, no electrophysiological data have been used in reconstruction methods and regularization schemes. In this study, we used a training set of simulated heart-surface potentials to create a realistic basis for reconstructions of electrical cardiac activity. We tested this method in computer simulations and in one patient. The quality of reconstruction improved significantly after projection of the results of traditional regularization methods on this new basis, both in silico (p<0.01) and in vivo (p<0.05). Thus, we demonstrate that the novel concept of applying electrophysiological data might be useful to improve noninvasive reconstruction of electrical heart activity.

Local field potentials (LFPs) represent the relatively slow varying components of the neural signal, and their analysis is instrumental in understanding normal brain function. To be properly analyzed, this signal needs to be separated in its fundamental frequency bands. Recent studies have shown that empirical mode decomposition (EMD) can be exploited to pre-process LFP recordings in order to achieve a proper separation. However, depending on the analyzed signal, EMD is known to generate components that may cover a too wide frequency range to be considered as narrow banded. As an alternative, we present here an improved version of the singular spectrum analysis (SSA) algorithm, validated by numerical simulations, and applied to LFP recordings in V1 of a macaque monkey exposed to simple visual stimuli. The components generated by the improved SSA algorithm are shown to be more meaningful than those generated by EMD, paving the way for its use in LFP analysis.

Local signaling domains and numerous interacting molecular pathways and substrates contribute to the whole-cell response of myocytes during β-adrenergic stimulation (βARS). We aimed to elucidate the quantitative contribution of substrates and their local signaling environments during βARS to the canine epicardial ventricular myocyte electrophysiology and calcium transient (CaT). We present a computational compartmental model of βARS and its electrophysiological effects. Novel aspects of the model include localized signaling domains, incorporation of β1 and β2 receptor isoforms, a detailed population-based approach to integrate the βAR and Ca(2+)/Calmodulin kinase (CaMKII) signaling pathways and their effects on a wide range of substrates that affect whole-cell electrophysiology and CaT. The model identifies major roles for phosphodiesterases, adenylyl cyclases, PKA and restricted diffusion in the control of local cAMP levels and shows that activation of specific cAMP domains by different receptor isoforms allows for specific control of action potential and CaT properties. In addition, the model predicts increased CaMKII activity during βARS due to rate-dependent accumulation and increased Ca(2+) cycling. CaMKII inhibition, reduced compartmentation, and selective blockade of β1AR is predicted to reduce the occurrence of delayed afterdepolarizations during βARS. Finally, the relative contribution of each PKA substrate to whole-cell electrophysiology is quantified by comparing simulations with and without phosphorylation of each target. In conclusion, this model enhances our understanding of localized βAR signaling and its whole-cell effects in ventricular myocytes by incorporating receptor isoforms, multiple pathways and a detailed representation of multiple-target phosphorylation; it provides a basis for further studies of βARS under pathological conditions., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Atrial fibrillation (AF) is a progressive arrhythmia which causes time dependent impairing of the cardiac muscle. This makes that proper therapeutic interventions depend on the degree of AF progression, i.e., on the temporal decrease of the organization of the electrical patterns observed during AF. Standard effective treatments are still lacking nowadays, and this calls for suitable noninvasive analysis of AF. In this sense, an appropriate therapy relies on the knowledge of AF characteristics, as its degree of organization. To this purpose, fast and accurate imaging of cardiac electrical activity can be helpful. Relying on the results of previous work on noninvasive assessment of the complexity of AF, we put forward a method to obtain visual maps of the topographic projection of the main atrial activity (AA) component given by principal component analysis, which is shown to provide detailed information about AA potential pattern distributions on the body surface. Different AA potential pattern distributions can then be identified, depending on the underlying degree of AF organization. An automated way to assess AF organization degree is then proposed, based on topographic projections. Similarities with previous studies suggest its usefulness for determining uniform distributions in the activation patterns on the body surface.