Ronald L. Westra

Local control of β-adrenergic stimulation: Effects on ventricular myocyte electrophysiology and Ca2+-transient

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.

Determinants of Beat-to-Beat Variability of Repolarization Duration in the Canine Ventricular Myocyte: A Computational Analysis

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.

Implementing Wavelets in Continuous-Time Analog Circuits With Dynamic Range Optimization

Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. A method is described to implement wavelets in analog circuits by fitting the impulse response of a linear system to the time-reversed wavelet function. The fitting is performed using local search involving an L2 criterion, starting from a deterministic starting point. This approach offers a large performance increase over previous Padé-based approaches and allows for the circuit implementation of a larger range of wavelet functions. Subsequently, using state-space optimization the dynamic range of the circuit is optimized. Finally, to illustrate the design procedure, a sixth-order L2-approximated orthonormal Gaussian wavelet filter using Gm-C integrators is presented.

Wavelet approximation for implementation in dynamic translinear circuits

Abstract For applications requiring low power consumption, signal processing in the analog domain is preferable. Approximate implementations of wavelet transforms in analog hardware can be achieved with dynamic translinear circuits. The quality of such implementations depends on the accuracy of the corresponding wavelet approximations. A design trade-off involves the approximation accuracy versus the complexity (model order) of the implemented filter. First we discuss the technique of Pade approximation for obtaining wavelet approximations. Then we present the technique of L 2 -approximation, which is conceptually more attractive but computationally more demanding. These techniques are compared by means of a worked example, involving Gaussian wavelet approximation and real measurements of an ECG signal. The L 2 -approximation approach is shown to exhibit superior performance.

Noninvasive reconstruction of cardiac electrical activity: update on current methods, applications and challenges

Electrical activity at the level of the heart muscle can be noninvasively reconstructed from body-surface electrocardiograms (ECGs) and patient-specific torso-heart geometry. This modality, coined electrocardiographic imaging, could fill the gap between the noninvasive (low-resolution) 12-lead ECG and invasive (high-resolution) electrophysiology studies. Much progress has been made to establish electrocardiographic imaging, and clinical studies appear with increasing frequency. However, many assumptions and model choices are involved in its execution, and only limited validation has been performed. In this article, we will discuss the technical details, clinical applications and current limitations of commonly used methods in electrocardiographic imaging. It is important for clinicians to realise the influence of certain assumptions and model choices for correct and careful interpretation of the results. This, in combination with more extensive validation, will allow for exploitation of the full potential of noninvasive electrocardiographic imaging as a powerful clinical tool to expedite diagnosis, guide therapy and improve risk stratification.

Analog complex wavelet filters

This paper presents an analog implementation of the complex wavelet transform using both the complex first order system (CFOS) and the Pade approximation. The complex wavelet filter design is based on the combination of the real and the imaginary state-space descriptions that implement the respective transfer functions. In other words, a complex filter is implemented by an ordinary state-space structure for the real part and an extra C matrix for the imaginary part. Several complex wavelets, such as Gabor, Gaussian and Morlet complex wavelets, are obtained and simulations demonstrate excellent approximations to the ideal wavelets.

Knowledge discovery and emergent complexity in bioinformatics

In February 1943, the Austrian physicist Erwin Schrodinger, one of the founding fathers of quantum mechanics, gave a series of lectures at the Trinity College in Dublin, entitled “What Is Life? The Physical Aspect of the Living Cell and Mind”. In these lectures Schrodinger stressed the fundamental differences encountered between observing animate and inanimate matter, and advanced some at the time audacious hypotheses about the nature and molecular structure of genes, some ten years before the discoveries of Watson and Crick.

Network topology of NaV1.7 mutations in sodium channel-related painful disorders

BackgroundGain-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.ResultsWe 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.ConclusionsOur 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.

Multiwavelet Design for Cardiac Signal Processing

An approach for designing multiwavelets is introduced, for use in cardiac signal processing. The parameterization of the class of multiwavelets is in terms of associated FIR polyphase all-pass filters. Orthogonality and a balanced vanishing moment of order 1 are built into the parameterization. An optimization criterion is developed to associate the wavelets with different meaningful segments of a signal. This approach is demonstrated on the simultaneous detection of QRS-complexes and T-peaks in ECG signals

The identification of dynamic gene-protein networks

In this study we will focus on piecewise linear state space models for gene-protein interaction networks. We will follow the dynamical systems approach with special interest for partitioned state spaces. From the observation that the dynamics in natural systems tends to punctuated equilibria, we will focus on piecewise linear models and sparse and hierarchic interactions, as, for instance, described by Glass, Kauffman, and de Jong. Next, the paper is concerned with the identification (also known as reverse engineering and reconstruction) of dynamic genetic networks from microarray data. We will describe exact and robust methods for computing the interaction matrix in the special case of piecewise linear models with sparse and hierarchic interactions from partial observations. Finally, we will analyze and evaluate this approach with regard to its performance and robustness towards intrinsic and extrinsic noise.