Multi-Scale Modeling of Vascular Signaling Units

血管信号单元的多尺度建模

• 批准号: 10406687
• 负责人: COLLEEN E CLANCY
• 金额: $ 22.73万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10406687
• 关键词: Address; Adoption; Antihypertensive Agents; Back; Biological; Biology; Blood Vessels; Calcium Signaling; Cardiovascular Diseases; Cell physiology; Cells; Client; Code; Collaborations; Collection; Communities; Complex; Computer Models; Computer software; Computers; Data; Data Analyses; Data Set; Documentation; Drops; Electrophysiology (science); Encapsulated; Engineering; Ensure; Environment; Feedback; Female; Foundations; Future; Goals; Hypertension; Image; Image Analysis; Individual; Intuition; Lead; Microscopy; Modality; Modeling; Modernization; Nomenclature; Online Systems; Output; Pathologic; Pharmaceutical Preparations; Publishing; Pythons; Quality Control; Reproducibility; Research; Research Personnel; Resolution; Running; Scientist; Secure; Sex Differences; Signal Transduction; Signaling Protein; Smooth Muscle; Smooth Muscle Myocytes; Software Engineering; Software Tools; Source Code; Standardization; Techniques; Testing; Training; Vascular Smooth Muscle; Visualization; Woman; analysis pipeline; base; biophysical properties; cardiovascular risk factor; cloud based; cost; data analysis pipeline; data curation; data modeling; data quality; design; experience; experimental study; file format; flexibility; graphical user interface; improved; improved functioning; in silico; interdisciplinary approach; interoperability; large scale data; male; men; model building; modifiable risk; mortality; multi-scale modeling; multimodal data; multimodality; nanoscale; novel; open source; parent grant; predictive modeling; programs; tool; web app; web page

Project Summary/Abstract Hypertension is one of the largest modifiable risk factors for cardiovascular disease, which is the leading cause of mortality for men and women. As more antihypertensive therapies become available, it has become clear that males and females respond differently to these treatments. Yet the mechanisms behind these sex differences are largely unknow. The parent grant aims to develop a multi-disciplinary approach to reveal the mechanisms of male and female hypertension and by building a detailed model to predict how drugs can differentially alter vascular function between these groups. This is being achieved by comparing male and female vascular smooth muscle cells using a range of state-of-the-art techniques including electrophysiology, Ca2+ imaging, nano-scale super-resolution microscopy, and detailed in silico predictive modelling. The goals of this supplemental project is to provide reliable, reproducible, and extensible analysis software that will unify these inherent multi-modal data sets. The output of these analysis pipelines will be used as input to computational models to ensure robust ground-truth predictions of key biophysical parameters. Each software component will have a fully documented back-end engine (written in Python) with an advanced-programming-interface (API) for interoperability, extendibility, and adoption by others. Cloud based computational and graphical-user-interfaces (GUIs) will be developed. These analysis pipelines will be hardened by using engineering standards for versioning, unit testing, code documentation, and interoperability with existing software. For these tools to be accessible and used by others, we will use agreed upon file formats and biological nomenclature standards. For these pipelines to be discoverable, they will be accessibility through multiple channels including online open-source code sharing and installation from package managers. To ensure one-click access for other researchers, the pipelines will be containerized for easy and reliable installation, allowing the same code to be run on individual computers, local cluster, or in the cloud. By following best software engineering principles, we aim to create a multi-modal analysis pipeline that seamlessly facilitate collaborations between groups of researchers and allow the sharing and publishing of data and analysis with the greater scientific community and ultimately the public.

项目摘要/摘要 高血压是心血管疾病的最大可修改风险因素之一，这是 男女死亡的主要原因。随着较高的抗高血压疗法 可用，很明显男性和女性对这些治疗方法的反应不同。 然而，这些性别差异背后的机制在很大程度上是未知的。父母赠款的目的是 开发一种多学科方法来揭示男性和女性高血压的机制 并通过建立详细的模型来预测药物如何差异化血管功能 在这些组之间。这是通过比较男性和雌性血管平滑来实现的 肌肉细胞使用一系列最新技术，包括电生理学，CA2+ 成像，纳米级超分辨率显微镜，并在计算机预测建模中详细介绍。这 这个补充项目的目标是提供可靠，可重复和可扩展的分析 将统一这些固有的多模式数据集的软件。这些分析管道的输出 将用作计算模型的输入，以确保密钥的强大基础真相预测 生物物理参数。每个软件组件将具有完整的后端引擎 （用python编写）带有高级编程 - 接口（API），用于互操作性， 扩展性和他人的采用。基于云的计算和图形 - 用户界面 （GUIS）将开发。这些分析管道将通过使用工程来硬化 与现有的版本控制，单元测试，代码文档和互操作性的标准 软件。对于这些工具可以被他人访问和使用，我们将使用同意的文件格式使用 和生物命名标准。这些管道是可发现的，它们将是 通过多个渠道的可访问性，包括在线开源代码共享和安装 来自包装经理。为了确保其他研究人员的一键式访问，管道将是 容器以简化可靠的安装，允许在个人上运行相同的代码 计算机，本地群集或云中。通过遵循最佳软件工程原则，我们的目标 创建一个多模式分析管道，该管道无缝地协作组之间的协作 研究人员的分享和发布数据和分析与更大的科学 社区，最终是公众。