雷诺定受体建模：从心肌钙循环到心律失常机制

高颖; 张宇澄; 王文尧; 苏煊怡; 宋震

doi:10.11999/JEIT250957

雷诺定受体建模：从心肌钙循环到心律失常机制

doi: 10.11999/JEIT250957 cstr: 32379.14.JEIT250957

高颖^{1, 2},
张宇澄¹,
王文尧³,
苏煊怡³,
宋震^1, ,

1.
鹏城实验室深圳 518000
2.
南方科技大学深圳 518055
3.
北京大学第三医院心内科血管医学研究所北京 100096

基金项目: 国家自然科学基金(82172067)

详细信息

作者简介:
高颖：女，博士生，研究方向为心脏电生理、多尺度建模等

张宇澄：男，博士，助理研究员，研究方向为心脏电生理、多尺度建模等

王文尧：男，博士，研究员，研究方向为心血管疾病等

苏煊怡：女，硕士生，研究方向为心血管疾病等

宋震：男，博士，副研究员，研究方向为心脏电生理、多尺度建模等

通讯作者:
宋震　songzh01@pcl.ac.cn

中图分类号: TP391.9
计量
- 文章访问数: 49
- HTML全文浏览量: 30
- PDF下载量: 4
- 被引次数: 0
出版历程
- 修回日期: 2025-12-29
- 录用日期: 2025-12-29
- 网络出版日期: 2026-01-08

Progress in Modeling Cardiac Myocyte Calcium Cycling and Investigating Arrhythmia Mechanisms: A Study Focused on the Ryanodine Receptor

1.
Pengcheng Laboratory, Shenzhen 518000, China
2.
Southern University of Science and Technology, Shenzhen 518055, China
3.
Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing 100096, China

Funds: The National Natural Science Foundation of China(82172067)

摘要

摘要: 雷诺定受体(RyR)是调控心肌细胞钙稳态的关键蛋白，主要介导肌浆网的钙离子释放。RyR的功能异常，无论是过度激活还是功能减弱，均可触发异常钙释放，诱发早期后去极化(EADs)和/或延迟后去极化(DADs)，这是心律失常发生和发展的重要机制。为深入探究RyR在生理及病理状态下的行为特性，研究者已开发并广泛应用多种融合其随机门控特性的数学与计算模型。本文系统梳理了RyR的结构特征和关键生理功能，重点归纳了其建模策略，总结了RyR模型在心肌细胞钙循环模型中的整合研究进展及其在不同类型心肌细胞中的应用，并深入剖析了RyR功能异常介导心律失常的机制及其靶向药物的研发现状。进一步地，本文讨论了人工智能(AI)、数字孪生等新兴方法在 RyR 建模中的潜在作用，并对现有模型的适用性与发展方向进行了展望。
- 雷诺定受体 /
- 心肌细胞 /
- 钙循环 /
- 心律失常
Abstract: Significance Ryanodine receptor (RyR) is an essential regulator of cardiac intracellular calcium homeostasis by controlling the release of Ca²⁺ from the sarcoplasmic reticulum (SR). Its functional abnormalities, such as overactivation or impaired activity, are critical mechanisms underlying early and delayed afterdepolarizations, significantly increasing the risk of arrhythmias. The dynamic coupling between electrical activity and calcium cycling in cardiomyocytes involves highly dynamic and spatially organized processes that are challenging to fully capture experimentally. Conventional experimental techniques, such as animal models and pharmacological studies, are limited by high costs and difficulties in controlling variables. As a result, developing mathematical models and computer simulations of the RyR has become a crucial approach for investigating RyR function regulation under physiological and pathological conditions, as well as its arrhythmogenic mechanisms. This review provides a systematic overview of RyR biology and modeling. It begins by synthesizing RyR structural features and fundamental functional properties to establish a mechanistic basis for gating and regulation. Next, it evaluates contemporary and emerging modeling techniques, outlining the merits and limitations of various computational approaches. The review then summarizes the integration of RyR models into cardiac Ca²⁺ cycling frameworks and their applications across cardiomyocyte subtypes. Furthermore, the review covers arrhythmogenic mechanisms arising from RyR dysfunction and examines targeted drug therapies designed to normalize channel activity. Finally, it highlights artificial intelligence and cardiac digital twins as emerging paradigms for advancing RyR modeling and therapeutic applications. Progress The accumulation of RyR structural data has driven continuous innovation in modeling strategies. Early models often used phenomenological strategies that were practical but mechanistically limited. Markov models now represent the dominant computational framework for simulating RyR gating behavior, enabling detailed replication of calcium sparks and other key events through discrete state transitions. A key advantage of deterministic integration over other numerical methods for solving Markov models is its superior computational efficiency and remarkable flexibility in adapting to diverse cardiomyocyte types. However, it ignores the stochastic nature of RyR opening and fails to reproduce stochastic fluctuations in intracellular calcium concentration, potentially leading to discrepancies between simulations and physiological reality. In contrast, stochastic Markov models can capture these random behaviors, which are critical for investigating arrhythmogenic phenomena like calcium waves. However, they necessitate substantial experimental data and considerable computational resources, consequently hindering their broader-scale application. The development of artificial intelligence methods, including the use of deep neural networks to compress Markov models into single equations, has substantially improved computational efficiency. Meanwhile, structural biology advances have clarified the conformational dynamics of RyRs and subunit cooperativity in gating, especially in diastolic calcium leak, prompting more detailed models like those incorporating subunit interactions or molecular dynamics. Additionally, various RyR models have been successfully integrated into cardiac action potential frameworks, serving as powerful tools for investigating arrhythmogenic mechanisms like delayed afterdepolarizations (DADs) and early afterdepolarizations (EADs). These models not only enhance the understanding of electrical disturbances caused by RyR dysfunction but also provide a valuable platform for antiarrhythmic drug screening and mechanistic research. Conclusion Several RyR models have been developed that accurately simulate essential physiological processes such as calcium sparks, enabling broad application in cardiomyocyte calcium dynamics studies. However, current modeling efforts face considerable challenges:(1) Lack of a unified modeling framework. There is still no unified RyR model capable of accurately simulating calcium dynamics across the wide spectrum of physiological and pathological conditions. To select appropriate model for intracellular calcium handling, careful evaluation of the specific effects of different models is necessary. (2) Computational burden restricts multiscale integration. While multiscale models are essential to bridge arrhythmic mechanisms from cellular calcium dynamics to tissue-level propagation by incorporating heterogeneity, their high computational cost presents a formidable barrier to scaling for clinically relevant applications. (3) Underdeveloped pacemaker cell models. Existing research focuses largely on ventricular and atrial myocytes, while pacemaker cell models are relatively underdeveloped and often employ “common pool” approximations that fail to capture spatial calcium gradients. Future research should therefore prioritize the development of detailed pacemaker cell models that represent calcium release unit (CRU) networks and incorporate realistic RyR dynamics. While still in early stages of development for RyR modeling, emerging approaches like artificial intelligence and cardiac digital twins thus offer substantial potential to advance both mechanistic understanding and applications in precision medicine. Prospects The future of RyR research will increasingly rely on combining multidisciplinary advances across structural biology, biophysics, and computational science. Integrative efforts are essential to bridge molecular-scale conformational changes of RyR to organ-level cardiac function, which will enable the creation of scalable and clinically actionable models that not only deepen mechanistic insight but also accelerate translational innovation in precision cardiology. Emerging tools like AI and cardiac digital twins offer a pathway toward clinically relevant, multi-scale cardiac models that incorporate patient-specific electrophysiology and calcium handling. Such models could profoundly improve our understanding of arrhythmia mechanisms and heart failure pathophysiology, while also serving as predictive platforms for mechanism-based personalized antiarrhythmic therapy development.
- ryanodine receptor /
- cardiac myocyte /
- calcium cycling /
- arrhythmias

HTML全文

图 1 RyR建模策略及应用概览

图中展示的RyR结构图来自Peng等人^[14]对猪心脏RyR2冷冻电镜结构解析

下载: 全尺寸图片幻灯片

图 2 马尔可夫模型代表方案

二状态模型改编自Sobie等人^[69]；三状态模型改编自Hinch等人^[52]和Mukherjee等人^[67](从左至右)；四状态模型改编自Keizer等人^[54]、Stern等人^[55]和Restrepo等人^[56](从左至右)

下载: 全尺寸图片幻灯片

图 3 RyR在不同类型心肌细胞中的作用

下载: 全尺寸图片幻灯片

表 1 不同类型的RyR模型

模型类别	代表方案	核心思想	主要优势	主要局限	代表模型
现象学模型	经验公式模型	根据观测拟合经验关系	简单/易耦合	机制解释性弱	Luo-Rudy模型^[46]
	HH范式模型	门控变量表征激活/失活	直观关联CICR机制	未涵盖多态转换	Chudin模型^[49]
	统计火花模型	以Ca火花统计分布建模	参数可测	难以扩展研究	Shiferaw模型^[50]
马尔可夫模型	二状态模型	仅含“开/关”状态	结构简单	无适应/失活	Cannell模型^[51]
	三状态模型	引入适应态或新关闭态	可模拟不应期	多重调控刻画弱	Hinch模型^[52]、Greene-Shiferaw模型^[53]
	四状态模型	包含多个状态	生理适配性强	参数多且依赖特定条件	Keizer模型^[54]、Stern模型^[55]、Restrepo模型^[56]
亚基间协同性模型	Monod-Wyman-Changeux模型	亚基结合改变整体构象	机制清晰、参数简洁	无法模拟亚基异步的状态转换	Zahradnikova模型^[57]
亚基间协同性模型	基于马尔可夫框架的模型	各亚基独立变化，通过能量矩阵量化协同	灵活性强、物理直观	计算复杂度高	Wang模型^[58]、Greene模型^[59]
分子动力学模型	-	基于冷冻电镜结构进行全原子模拟	分辨率高、靶向精准	计算成本极高	Greene模型^[60]、Dal Cortivo模型^[61]

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