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      Beatbox—A Computer Simulation Environment for Computational Biology of the Heart

      ,

      Visions of Computer Science - BCS International Academic Conference (VOCS)

      BCS International Academic Conference

      22 - 24 September 2008

      Computational biology, Cardiac electrophysiology, High Performance Computing

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Despite over a century’s study, the trigger mechanisms of cardiac arrhythmias are poorly understood. Even modern experimental methods do not provide sufficient temporal and spacial resolution to trace the development of fibrillation in samples of cardiac tissue, not to mention the heart in vivo. Advances in human genetics provide information on the impact of certain genes on cellular activity, but do not explain the resultant mechanisms by which fibrillation arises. Thus, for some genetic cardiac diseases, the first presenting symptom is death.

          Computer simulations of electrical activity in cardiac tissue offer increasingly detailed insight into these phenomena, providing a view of cellular-level activity on the scale of a whole tissue wall. Already, advances in this field have led to developments in our understanding of heart fibrillation and sudden cardiac death and their impact is expected to increase significantly as we approach the ultimate goal of whole-heart modelling.

          Modelling the propagation of Action Potential through cardiac tissue is computationally expensive due to the huge number of equations per cell and the vast spacial and temporal scales required. The complexity of the problem encompasses the description of ionic currents underlying excitation of a single cell through the inhomogeneity of the tissue to the complex geometry of the whole heart. The timely running of computational models of cardiac tissue is increasingly dependant on the effective use of High Performance Computing (HPC), i.e. systems with parallel processors. Current state of the art cardiac simulation tools are limited either by the availability of modern, detailed models, or by their hardware portability or ease of use. The miscellany of current model implementations leads many researchers to develop their own ad-hoc software, preventing them from both utilising the power of HPC effectively, and from collaborating fluidly. It is, arguably, impeding scientific progress.

          This paper presents a roadmap for the development of Beatbox, a computer simulation environment for computational biology of the heart—an adaptable and extensible framework with which High Performance Computing may be harnessed by researchers.

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          Most cited references 2

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          Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation.

          Functional properties of the myocardium are mediated by the tissue structure. Consequently, proper physiological studies and modeling necessitate a precise knowledge of the fiber orientation. Magnetic resonance (MR) diffusion tensor imaging techniques have been used as a nondestructive means to characterize tissue fiber structure; however, the descriptions so far have been mostly qualitative. This study presents a direct, quantitative comparison of high-resolution MR fiber mapping and histology measurements in a block of excised canine myocardium. Results show an excellent correspondence of the measured fiber angles not only on a point-by-point basis (average difference of -2.30 +/- 0.98 degrees, n = 239) but also in the transmural rotation of the helix angles (average correlation coefficient of 0.942 +/- 0.008 with average false-positive probability of 0.004 +/- 0.001, n = 24). These data strongly support the hypothesis that the eigenvector of the largest MR diffusion tensor eigenvalue coincides with the orientation of the local myocardial fibers and underscore the potential of MR imaging as a noninvasive, three-dimensional modality to characterize tissue fiber architecture.
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            Using computer models to understand the roles of tissue structure and membrane dynamics in arrhythmogenesis

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              Author and article information

              Contributors
              Conference
              September 2008
              September 2008
              : 99-109
              Affiliations
              University of Liverpool

              Computer Science Department

              Ashton Building

              Ashton Street

              Liverpool

              L69 3BX, UK
              Article
              10.14236/ewic/VOCS2008.10
              © Ross McFarlane et al. Published by BCS Learning and Development Ltd. Visions of Computer Science - BCS International Academic Conference

              This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

              Visions of Computer Science - BCS International Academic Conference
              VOCS
              Imperial College, London, UK
              22 - 24 September 2008
              Electronic Workshops in Computing (eWiC)
              BCS International Academic Conference
              Product
              Product Information: 1477-9358BCS Learning & Development
              Self URI (journal page): https://ewic.bcs.org/
              Categories
              Electronic Workshops in Computing

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