A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research.
A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research.
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DOI：
10.3389/fphys.2018.00958
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发表时间：
2018
影响因子：
4
通讯作者：
Grandi E
中科院分区：
文献类型：
--
作者：
Ni H;Morotti S;Grandi E
在心脏电生理学中，存在许多人与人之间和人内变异性的来源。这包括条件和环境的可变性，以及基因类型和分子多样性，包括离子通道和转运体的表达和行为差异，这导致表型多样性(例如，在细胞、组织和器官水平上的可变综合反应)。这些变异在心脏病和心律失常综合征的进展和治疗干预的结果中起着重要的作用。然而，传统的研究心律失常的电子计算机框架是建立在参数/特性平均方法的基础上的，这种方法通常忽略了生理多样性。在遗传学和神经科学工作的启发下，心脏电生理学的新建模框架最近被开发出来，这些框架利用现代计算能力和方法，并考虑到它们打算阐明的生物学数据的差异。在这篇综述中，我们概述了考虑生理变异性的统计和计算技术的最新进展，并超越了传统的心脏建模方案，该方案涉及在构建高度调整的复合模型时对来自多个个体的样本进行平均。我们讨论了这些先进的方法如何利用大数据(模拟)的力量来研究心律失常的机制，特别是房颤，并改进了对心律失常风险和药物反应的评估。还讨论了在具有可变性的硅胶方法中使用的挑战，并提出了未来的方向。
In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.
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