Grid Based Modeling of Electrical Propagation in Excitable Tissue

可兴奋组织中电传播的基于网格的建模

• 批准号: 7665306
• 负责人: Jack W Buchanan
• 金额: $ 18.5万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7665306
• 关键词: Adopted; Anatomy; Architecture; Arrhythmia; Biological; Biomedical Engineering; Brain; Cellular Membrane; Characteristics; Code; Collaborations; Communication; Computers; Coupling; Development; Dimensions; Disease; Elements; Environment; Epilepsy; Extensible Markup Language; Extracellular Space; Fiber; Gastrointestinal tract structure; Health Sciences; Heart; Human; Individual; International; Ischemia; Knowledge; Language; Lateral; Libraries; Link; Mathematics; Measures; Membrane; Metabolism; Methods; Modeling; Muscle; Nature; Nerve; Neurosciences; Performance; Physiological; Physiology; Property; Rest; Running; Science; Speed; Techniques; Technology; Time; Tissues; Universities; base; cluster computing; computer code; distributed memory; extracellular; interest; membrane model; motility disorder; multi-scale modeling; multicore processor; open source; parallel computer; parallel computing; performance site; public health relevance; shared memory; simulation; software development; success; tool

DESCRIPTION (provided by applicant): We propose to develop, construct, and refine mathematical and computational multi-scale models which will link macroscopic electrical impulse propagation in the heart to underlying membrane-based sub-cellular ionic currents and other intercellular and intracellular metabolic processes, in ways which preserve anatomical architecture of the heart. Such a model will incorporate more realistic physiology and anatomy while avoiding many of the spatial averaging problems of current bi-domain models. Individual sub-cellular and membrane parameters cannot be measured during propagation. By creating propagating models which include these parameters, we seek to create a new understanding of electrical impulse propagation in the heart and in other excitable tissue, such as nerve and muscle. Moreover similar techniques can also be used to model abnormal electrical activity in the brain, which contributes to epilepsy, and abnormal electrical activity in the gastrointestinal tract, a major cause of GI motility disorders. Most previous large scale models of this type have incorporated various simplifications of the cellular architecture in the interest of computational efficiency. Such assumptions have produced results which have not always withstood close experimental scrutiny. In order to allow for fundamentally important tissue architectural complexities, we will continue the development of new modeling techniques which bridge across scales and which employ high order explicit time-integrators so that they can run efficiently using parallel computation on distributed memory clusters of multiprocessors. This will allow for efficient simulations of an entire ventricle or whole heart without averaging out the effects of the discrete cellular nature of the heart. Since both the sub-cellular and macro aspects of these studies can be treated with varying degrees of complexity, we will incorporate modular libraries, starting with the library developed using cellML, an XML derived modeling language, under the IUPS Physiome project. These modular libraries can be used to make trade-offs between complexity and speed of execution which are appropriate for a given model. We have already had substantial success at employing newer explicit numerical integration techniques in both physical and biological problems. In order to further exploit these newer techniques in a biological environment in a way which is efficient and scalable, new university collaborations have been formed between the Biomedical Engineering Department, located at the Health-Science Center in Memphis, and the Mathematics Department, located on the main campus in Knoxville. We will create a modest sized cluster of parallel computers at each of the performance sites with the ability to link them across the network with very high performance communications channels. Software developed in this more coarsely linked, clustered environment will be useful for constructing even larger scale models to run in computational grid environments of hundreds to thousands of processors and associated distributed memory. PUBLIC HEALTH RELEVANCE After 50 years of experimentation and modeling, it is still not known whether lethal cardiac arrhythmias usually arise from a single "irritable" focus (enhanced automaticity) and are then propagated to the rest of the heart or, alternatively, that the primary abnormality is a disorder of propagation itself (reentry). Similarly, in the neurosciences, it is not really known how often epilepsy is due to a single "irritable" focus in the brain and how often the primary disorder is in the propagation of impulses through connecting fibers of the brain itself. We seek support to develop modeling tools based on newer mathematical techniques and newer knowledge of anatomy and physiology to try to answer these questions.

描述（由申请人提供）：我们建议发展，构建和完善数学和计算的多尺度模型，这些模型将宏观电脉冲在心脏中的传播与潜在的基于膜的亚细胞离子电流和其他细胞间和细胞内代谢过程联系起来，以保持心脏的解剖结构。这样的模型将包含更真实的生理和解剖学，同时避免了当前双域模型的许多空间平均问题。在繁殖过程中不能测量单个亚细胞和膜参数。通过创建包含这些参数的传播模型，我们试图对电脉冲在心脏和其他可兴奋组织（如神经和肌肉）中的传播产生新的理解。此外，类似的技术也可以用于模拟大脑中的异常电活动，这有助于癫痫，以及胃肠道中的异常电活动，这是胃肠道运动障碍的主要原因。为了提高计算效率，以前大多数这种类型的大尺度模型都结合了对细胞结构的各种简化。这样的假设所产生的结果并不总是经得起仔细的实验检验。为了考虑基本重要的组织架构复杂性，我们将继续开发新的建模技术，这些技术跨越尺度，并采用高阶显式时间积分器，以便它们可以在多处理器的分布式内存集群上使用并行计算有效地运行。这将允许有效地模拟整个心室或整个心脏，而不会平均心脏离散细胞性质的影响。由于这些研究的亚细胞和宏观方面都可以用不同程度的复杂性来处理，因此我们将合并模块化库，从IUPS Physiome项目下使用cellML（一种XML派生的建模语言）开发的库开始。这些模块化库可用于在复杂性和执行速度之间进行权衡，这适合给定的模型。我们已经在物理和生物问题中使用新的显式数值积分技术取得了实质性的成功。为了在生物环境中以高效和可扩展的方式进一步利用这些新技术，位于孟菲斯健康科学中心的生物医学工程系和位于诺克斯维尔主校区的数学系之间形成了新的大学合作关系。我们将在每个表演场地创建一个中等规模的并行计算机集群，以便通过网络通过高性能通信通道将它们连接起来。在这种连接更为粗糙的集群环境中开发的软件，对于构建更大规模的模型非常有用，这些模型可以在由数百到数千个处理器和相关的分布式内存组成的计算网格环境中运行。