Remodeling of Pulmonary Cardiovascular Networks in the Presence of Hypertension

研究小组将开发用于肺动脉高压研究的数学和计算模型。这是一种罕见但进展迅速的心血管疾病，死亡率很高。 肺动脉高压涉及血压升高以及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位置具有挑战性。模型将结合合作者提供的实验数据来开发。这些数据将通过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血压的侵入性测量来获得。 基于流体力学、固体力学、网络分析、反问题和参数估计的建模和方法，将捕获心室、大血管、小血管网络及其相互作用的影响。所提出的肺心血管模型有可能被纳入使用非侵入性测量预测压力的诊断方案中，以减少侵入性后续程序的数量，并作为识别与疾病诊断类别和疾病进展程度相关的特征的重要组成部分。 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、大肺动脉和小肺动脉和静脉，并解释由于肺动脉高压导致的系统组件的变化。将设计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，可以集成到流体动力学模型中，并进行线性化以促进从大型容器到小型容器的过渡。 还将提供第二个基于生理学的右心室模型，该模型结合了简单弹性函数和单纤维模型的思想。该模型将能够预测弹性作为右心室厚度的函数。分析将重点关注肺循环，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流量和压力波形，预测与疾病进展相关的特征，并通过敏感性分析和参数估计，使模型针对患者特定。 通过这些创新，将有可能使用一维系统级模型与非侵入性流量测量中的肺动脉血压相结合，来评估与肺动脉高压相关的疾病进展，同时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.