The Best of Both: Toward a hybrid discrete and continuum multiscale platelet aggregation and coagulation model

两者的优点：建立混合离散和连续多尺度血小板聚集和凝血模型

• 批准号: 1521748
• 负责人: Robert Kirby
• 金额: $ 44.98万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521748&HistoricalAwards=false
• 关键词: Best; Both; Toward; hybrid; discrete

This project brings together computational scientists and mathematical modelers to solve a fundamental multifaceted multiscale problem in human physiology, namely understanding the platelet aggregation and coagulation (PAC) processes that comprise blood clotting. Over the past two decades, mathematical biology experts, including the current investigators, have worked to apply sound mathematical modeling principles and computational methods to attempt to dissect the complex interactions that occur within the platelet aggregation and coagulation process - fluid-structure interactions, mechanical-chemical interactions, and structure-structure interactions, to name a few. The complexity of this problem is due to its multiscale nature in both space and time, the complex disparate physical and chemical processes involved, as well as the challenge of attempting to model processes for which experimental validation is difficult. Over recent years, the experimental world has made significant advances in accumulating data that might both help us model the PAC cascade more faithfully, and allow us to predict its pathological deviations. The challenge is in connecting these two worlds and this project has as its goal employed modern computing concepts (numerical and algorithmic) to meet this challenge. While this project focuses on the PAC cascade, it will also have impact in a wide range of multiscale, multidiscipline applications such as chemical engineering and material science.The physiological time scale for PAC is on the order of minutes. To date, the only model able to simulate the entire PAC process over such time scales is a meso-scale (continuum) model developed by co-PI Fogelson and collaborators. This capability comes at the cost of coarse-graining the geometry and mechanics of the PAC process. The co-PI has also developed a fine-grained model of platelet aggregation that is at the forefront of platelet modeling. However, while conceptually faithful to the mechanics of the PAC process and the geometric intricacies of the developing aggregates, the fine-grained model does not yet contain treatment of the chemical processes of coagulation. Further, this model is computationally expensive and can, in a reasonable amount of time, only simulate a small fraction of the physical time that the meso-scale model can. Consequently, the goals of this project are two-fold: first, to extend the simulation capabilities of the fine-grained model both in terms of conceptual fidelity to the PAC cascade, and also in terms of computational efficiency by implementation on hybrid (CPU/GPU) architectures; and second, to cross-validate in the multiscale context current meso-scale and the extended fine-scale PAC models. Accomplishing these goals is critical to our longer-term research objective of developing hybrid multiscale models - enabled by current and future experimental data - that combine the best features of both these models.

该项目汇集了计算科学家和数学建模者，以解决人类生理学中的一个基本的多方面多尺度问题，即理解包括血液凝固的血小板聚集和凝血（PAC）过程。在过去的二十年里，数学生物学专家，包括目前的研究人员，一直致力于应用合理的数学建模原理和计算方法，试图剖析血小板聚集和凝血过程中发生的复杂相互作用-流体-结构相互作用，机械-化学相互作用和结构-结构相互作用，仅举几例。这个问题的复杂性是由于其在空间和时间上的多尺度性质，所涉及的复杂的不同的物理和化学过程，以及试图模拟实验验证困难的过程的挑战。近年来，实验界在积累数据方面取得了重大进展，这些数据既可以帮助我们更忠实地模拟PAC级联反应，也可以让我们预测其病理学偏差。挑战在于连接这两个世界，该项目的目标是采用现代计算概念（数值和算法）来应对这一挑战。虽然该项目的重点是PAC级联，但它也将在化学工程和材料科学等广泛的多尺度，多学科应用中产生影响。PAC的生理时间尺度为分钟。迄今为止，唯一能够模拟整个PAC过程的模型是由共同PI Fogelson和合作者开发的中尺度（连续）模型。这种能力是以PAC工艺的几何形状和机械结构粗粒化为代价的。co-PI还开发了血小板聚集的细粒度模型，该模型处于血小板建模的最前沿。然而，虽然在概念上忠实于PAC过程的力学和发展中的聚集体的几何复杂性，细粒度模型还不包含混凝的化学过程的治疗。此外，该模型在计算上是昂贵的，并且在合理的时间量内，只能模拟中尺度模型所能模拟的物理时间的一小部分。因此，这个项目的目标是双重的：第一，扩展的细粒度模型的模拟能力，无论是在概念上的保真度PAC级联，也在计算效率方面的混合（CPU/GPU）架构上的实现;和第二，交叉验证在多尺度上下文中当前的中尺度和扩展的细尺度PAC模型。实现这些目标对于我们开发混合多尺度模型的长期研究目标至关重要-通过当前和未来的实验数据实现-联合收割机结合了这两种模型的最佳功能。