CRII: OAC: A computational framework for multiscale simulation of cardiovascular disease progression connecting cell-scale biology to organ-scale hemodynamics
CRII:OAC:将细胞尺度生物学与器官尺度血流动力学连接起来的心血管疾病进展多尺度模拟的计算框架
基本信息
- 批准号:2246911
- 负责人:
- 金额:$ 17.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-10-01 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Developing computer models of cardiovascular disease growth requires an organ-scale model of blood flow, a cell-scale model of cell biology, and a framework to couple these models. Such computer models could help predict cardiovascular disease by simulating spatial and temporal patterns of disease growth. To develop these models, we need a software infrastructure that integrates modeling techniques from multiple disciplines. This project will develop software for this purpose and will apply the software to the calcific aortic valve disease (CAVD) problem, which is prevalent in aging adults. The project will also help to understand the interaction between the fundamental biological and mechanical processes involved in CAVD. The project leverages existing resources at Northern Arizona University to perform outreach activities targeting underrepresented minority students in the region. The outcome of this study will contribute to advancing the national health and improving our scientific understanding of the interaction between different processes in cardiovascular disease. During the past two decades, significant advances have been made in the development of organ-scale patient-specific computational models of cardiovascular disease. These models often focus on a specific spatial and temporal scale and their goal is to quantify biomechanical biomarkers of cardiovascular disease growth. However, quantitative information about disease growth over long time scales is missing in these models. The goal of this project is to 1) Develop a software platform for multiscale two-way coupling between organ-scale biomechanics and cell-scale systems biology models. 2) Apply the computational framework to study the long-term spatial and temporal progression of CAVD. The organ-scale model will be based on continuum solid and fluid mechanics models, and the cell-scale model will be based on systems of differential equations. The developed software infrastructure could be applied to patient-specific data to model disease progression patterns. This will enable the development of transformative models that advance our knowledge of cardiovascular disease. The project will train highly interdisciplinary researchers at the interface of software development, biomechanics, and biology. Outreach activities will promote STEM participation by demonstrating the beauty of computer science and engineering blended and applied to biomedical applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
开发心血管疾病增长的计算机模型需要一个器官尺度的血流模型,一个细胞尺度的细胞生物学模型,以及一个耦合这些模型的框架。这种计算机模型可以通过模拟疾病增长的空间和时间模式来帮助预测心血管疾病。为了开发这些模型,我们需要一个软件基础设施,它集成了来自多个学科的建模技术。该项目将为此目的开发软件,并将该软件应用于钙化性主动脉瓣疾病(CAVD)问题,这是老年人中普遍存在的问题。该项目还将有助于了解CAVD中涉及的基本生物和机械过程之间的相互作用。该项目利用北方亚利桑那大学的现有资源,针对该地区代表性不足的少数民族学生开展外联活动。这项研究的结果将有助于促进国民健康,提高我们对心血管疾病不同过程之间相互作用的科学认识。在过去的二十年中,在心血管疾病的器官尺度患者特异性计算模型的发展方面取得了重大进展。这些模型通常专注于特定的空间和时间尺度,其目标是量化心血管疾病生长的生物力学生物标志物。然而,在这些模型中,缺少关于长时间尺度上疾病增长的定量信息。本项目的目标是:1)开发一个用于器官尺度生物力学和细胞尺度系统生物学模型之间多尺度双向耦合的软件平台。2)应用计算框架研究CAVD的长期时空进展。器官尺度模型将基于连续固体和流体力学模型,细胞尺度模型将基于微分方程系统。开发的软件基础设施可以应用于患者特定的数据,以模拟疾病进展模式。这将使我们能够开发出变革性的模型,从而提高我们对心血管疾病的认识。该项目将在软件开发,生物力学和生物学的界面上培养高度跨学科的研究人员。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Fluid-structure coupled biotransport processes in aortic valve disease
- DOI:10.1016/j.jbiomech.2021.110239
- 发表时间:2021-01-27
- 期刊:
- 影响因子:2.4
- 作者:Sadrabadi, Mohammadreza Soltany;Hedayat, Mohammadali;Arzani, Amirhossein
- 通讯作者:Arzani, Amirhossein
Multiscale modeling of tissue growth and remodeling coupled with mechanosensitive cell-scale systems biology
组织生长和重塑的多尺度建模与力敏感细胞尺度系统生物学相结合
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Sadrabadi, M.
- 通讯作者:Sadrabadi, M.
Aortic valve dynamics coupled with growth and remodeling due to aging and calcification
主动脉瓣动力学与衰老和钙化导致的生长和重塑相结合
- DOI:
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Sadrabadi, M.
- 通讯作者:Sadrabadi, M.
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Amirhossein Arzani其他文献
Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond
- DOI:
10.1007/s10439-022-02967-4 - 发表时间:
2022-04-20 - 期刊:
- 影响因子:5.400
- 作者:
Amirhossein Arzani;Jian-Xun Wang;Michael S. Sacks;Shawn C. Shadden - 通讯作者:
Shawn C. Shadden
Input parameterized physics informed neural networks for de noising, super-resolution, and imaging artifact mitigation in time resolved three dimensional phase-contrast magnetic resonance imaging
用于时间分辨三维相位对比磁共振成像中去噪、超分辨率和减轻成像伪影的输入参数化物理信息神经网络
- DOI:
10.1016/j.engappai.2025.110600 - 发表时间:
2025-06-15 - 期刊:
- 影响因子:8.000
- 作者:
Amin Pashaei Kalajahi;Hunor Csala;Zayeed Bin Mamun;Sangeeta Yadav;Omid Amili;Amirhossein Arzani;Roshan M. D’Souza - 通讯作者:
Roshan M. D’Souza
Transport and Mixing in Patient Specific Abdominal Aortic Aneurysms With Lagrangian Coherent Structures
具有拉格朗日相干结构的患者特异性腹主动脉瘤的运输和混合
- DOI:
10.1115/sbc2012-80475 - 发表时间:
2012 - 期刊:
- 影响因子:0
- 作者:
Amirhossein Arzani;S. Shadden - 通讯作者:
S. Shadden
Hemodynamics and Transport in Patient-specific Abdominal Aortic Aneurysms
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Amirhossein Arzani - 通讯作者:
Amirhossein Arzani
Flow topology and targeted drug delivery in cardiovascular disease.
心血管疾病中的流拓扑和靶向药物输送。
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:2.4
- 作者:
Sara S Meschi;Ali Farghadan;Amirhossein Arzani - 通讯作者:
Amirhossein Arzani
Amirhossein Arzani的其他文献
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{{ truncateString('Amirhossein Arzani', 18)}}的其他基金
Collaborative Research: Enhanced 4D-Flow MRI through Deep Data Assimilation for Hemodynamic Analysis of Cardiovascular Flows
合作研究:通过深度数据同化增强 4D-Flow MRI 用于心血管血流的血流动力学分析
- 批准号:
2246916 - 财政年份:2023
- 资助金额:
$ 17.5万 - 项目类别:
Standard Grant
EAGER: Understanding complex wind-driven wildfire propagation patterns with a dynamical systems approach
EAGER:通过动力系统方法了解复杂的风驱动野火传播模式
- 批准号:
2330212 - 财政年份:2023
- 资助金额:
$ 17.5万 - 项目类别:
Standard Grant
CAREER: Synergistic physics-based and deep learning cardiovascular flow modeling
职业:基于协同物理和深度学习的心血管血流建模
- 批准号:
2247173 - 财政年份:2022
- 资助金额:
$ 17.5万 - 项目类别:
Continuing Grant
CAREER: Synergistic physics-based and deep learning cardiovascular flow modeling
职业:基于协同物理和深度学习的心血管血流建模
- 批准号:
2143249 - 财政年份:2022
- 资助金额:
$ 17.5万 - 项目类别:
Continuing Grant
Collaborative Research: Enhanced 4D-Flow MRI through Deep Data Assimilation for Hemodynamic Analysis of Cardiovascular Flows
合作研究:通过深度数据同化增强 4D-Flow MRI 用于心血管血流的血流动力学分析
- 批准号:
2103434 - 财政年份:2021
- 资助金额:
$ 17.5万 - 项目类别:
Standard Grant
CRII: OAC: A computational framework for multiscale simulation of cardiovascular disease progression connecting cell-scale biology to organ-scale hemodynamics
CRII:OAC:将细胞尺度生物学与器官尺度血流动力学连接起来的心血管疾病进展多尺度模拟的计算框架
- 批准号:
1947559 - 财政年份:2020
- 资助金额:
$ 17.5万 - 项目类别:
Standard Grant
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