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

该项目将计算科学家和数学建模人员聚集在一起，以解决人类生理学中一个基本的、多方面的、多尺度的问题，即了解构成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来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它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，以及试图对难以进行实验验证的过程进行建模的挑战。近年来，实验界在积累数据方面取得了重大进展，这些数据可能有助于我们更准确地对PAC级联进行建模，并使我们能够预测其病理偏差。挑战在于连接这两个世界，这个项目的目标是使用现代计算概念(数值和算法)来迎接这一挑战。虽然该项目主要关注PAC级联，但它也将在广泛的多尺度、多学科应用中产生影响，如化学工程和材料科学。PAC的生理时间尺度约为分钟级。到目前为止，唯一能够在这样的时间尺度上模拟整个PAC过程的模型是由合作者Pi Fogelson和合作者开发的中尺度(连续)模型。这种能力是以粗粒化PAC过程的几何形状和机制为代价的。该联合PI还开发了一种处于血小板建模前沿的细粒度的血小板聚集模型。然而，尽管在概念上忠实于PAC过程的机制和正在形成的聚集体的几何复杂性，但细粒度模型还没有包含对凝聚的化学过程的处理。此外，这种模式的计算成本很高，而且在合理的时间内，只能模拟中尺度模式所能模拟的一小部分物理时间。因此，这个项目的目标有两个：第一，在概念上对PAC级联的保真度方面，以及在混合(CPU/GPU)架构上实现的计算效率方面，扩展细粒度模型的模拟能力；第二，在多尺度背景下交叉验证当前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--结合这两种模型的最佳特征。

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.