Remodeling of Pulmonary Cardiovascular Networks in the Presence of Hypertension

该研究小组将为肺动脉高压的研究开发数学和计算模型。这是一种罕见但进展迅速的心血管疾病，死亡率高。 肺动脉高压涉及血压升高和肺循环内血管壁硬度和厚度的变化。这些问题随着疾病的严重程度而恶化。诊断性疾病类别与肺循环网络的不同部分相关，但精确定位疾病起始的位置具有挑战性。模型将与合作者提供的实验数据一起开发。该数据将从血管血流和血管网络结构的非侵入性成像以及通过导管插入术获得的小鼠和人类肺血管中血压的侵入性测量中获得。 心腔、大血管、小血管网络及其相互作用的影响将基于流体力学、固体力学、网络分析、逆问题和参数估计的建模和方法学方法来捕获。所提出的肺心血管模型有可能被纳入诊断协议预测压力，使用非侵入性测量，以减少侵入性随访程序的数量，并作为一个重要组成部分，用于识别与疾病诊断类别和疾病进展程度相关的签名。 整个项目还为博士后、研究生和本科生在数据驱动的生物医学研究方面的综合培训提供了各种机会。将开发肺循环的系统级一维数学和计算流体动力学模型。这些模型将包括右心室、左心房、大小肺动脉和静脉，并考虑由于肺动脉高压导致的系统组件的改变。将设计一个基于生理学的动脉和静脉壁模型，该模型考虑胶原蛋白和弹性蛋白含量，并且还可以捕获响应于肺动脉高压的壁成分的重塑。该模型将植根于非线性弹性理论，并将产生更稳健的压力-面积关系，该关系可以集成在流体动力学模型中，并线性化以促进从大血管到小血管的过渡。 还将提供第二个基于生理学的右心室模型，其结合了简单弹性函数和单纤维模型的思想。该模型将能够预测作为右心室厚度的函数的弹性。分析将侧重于肺循环，但也将考虑对综合闭环模型中体循环建模的影响。在这两个目标中开发的模型将用于模拟和识别预测与疾病进展相关的特征的流量和压力波形，并通过敏感性分析和参数估计，使模型具有患者特异性。 通过这些创新，将有可能使用一维系统级模型与来自流量的非侵入性测量的肺动脉血压相结合，以评估与肺动脉高压相关的疾病进展，同时还减少侵入性测量的数量。

The research team will develop mathematical and computational models for the study of pulmonary hypertension. This is a rare but rapidly progressing cardiovascular disease with a high mortality rate. Pulmonary hypertension involves both elevated blood pressure and changes in vessel wall stiffness, and thickness within the pulmonary circulation. These issues worsen with the severity of the disease. Diagnostic disease categories are associated with different parts of the pulmonary circulation network, yet pinpointing locations where the disease initiates is challenging. Models will be developed in conjunction with the use of experimental data provided by collaborators. This data will be obtained from non-invasive imaging of vascular blood flow and vessel network structure, and from invasive measurements of blood pressure in pulmonary vessels in mice and humans obtained via catheterization. Effects of the heart chambers, large vessels, small vessel networks and their interactions will be captured based on modeling and methodological approaches from fluid mechanics, solid mechanics, network analysis, inverse problems and parameter estimation. The proposed pulmonary cardiovascular model has potential to be incorporated into diagnostic protocols predicting pressure using non-invasive measurements to reduce the number of the invasive follow-up procedures, and to serve as a vital component for identifying signatures associated with disease diagnostic categories and the degree of disease progression. The overall project also provides a variety of opportunities for integrated training of a postdoc, and graduate and undergraduate students, in data-driven biomedical research.System-level, one-dimensional mathematical and computational fluid dynamics models of the pulmonary circulation will be developed. These models will include the right ventricle, left atrium, the large and small pulmonary arteries and veins, and account for alterations in the system components due to pulmonary hypertension. A physiologically based arterial and venous wall model that accounts for collagen and elastin content and that can also capture remodeling of wall constituents in response to pulmonary hypertension will be designed. This model will be rooted in nonlinear elasticity theory and will yield a more robust pressure-area relation that can be integrated within the fluid dynamics model, and linearized to facilitate the transition from large to small vessels. A second physiologically based right ventricle model combining ideas from simple elastance functions and single-fiber models will also be provided. This model will enable prediction of elastance as a function of right ventricle thickness. The analysis will focus on the pulmonary circulation, but impacts on modeling systemic circulation in a comprehensive closed loop model will also be considered. The models developed in these two aims will be used to simulate and identify flow and pressure waveforms that predict features associated with disease progression and, via sensitivity analysis and parameter estimation, to render the model patient-specific. With these innovations it will be possible to use the one dimensional system level model combined with pulmonary arterial blood pressure from non-invasive measurements of flow to assess disease progression associated with pulmonary hypertension while also reducing the number of invasive measurements.