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Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running

机译:促进心律失常模拟:定量细胞自动机建模和并行运行的方法

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Background Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level. Methods We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG. Results We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given. Conclusions Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described.
机译:背景技术许多心律不齐是由离子通道和细胞水平的异常电活动触发的,然后在心脏内随时间变化。为了更好地了解心律不齐并通过心电图波形对其进行更准确的诊断,需要使用全心模型来探索通道/细胞水平的大规模平行活动与器官水平的综合电生理现象之间的关联。方法我们已经开发出一种方法,可以通过使用扩展的细胞自动机来构建大规模的电生理模型,并在共享存储机器的群集上运行此类模型。我们在这里描述该方法,包括扩展基于语言的细胞自动机以实现定量计算,使用Visible Human Project数据构建全心模型,在具有OpenMP的共享内存计算机集群上并行化模型。 MPI混合编程,以及将细胞活动与ECG关联的仿真算法。结果我们证明在我们扩展的细胞自动机系统中可以方便地追踪和捕获通道,细胞和器官水平的电活动。给出了一些使用二维切片模拟的ECG波形的示例,以支持ECG模拟算法。还给出了在四节点群集上对3-D模型的性能评估。结论具有扩展细胞自动机的定量多细胞建模是一种高效且广泛适用的方法,可以将不同级别的实验数据编织到计算模型中。此过程可用于研究复杂的集体生物活动,这些活动既不能通过其主导的微分方程来描述,也不能通过离散并行计算来描述。透明集群计算是使耗时的仿真可行的便捷有效的方法。作为典型案例,心律不齐可以通过上述方法有效地模拟。

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