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.

项目总结/摘要 高血压是心血管疾病最大的可改变的危险因素之一， 是男性和女性死亡的主要原因随着越来越多的抗高血压疗法 然而，很明显，男性和女性对这些治疗的反应不同。 然而，这些性别差异背后的机制在很大程度上是未知的。父母补助金的目的是 发展多学科的方法来揭示男性和女性高血压的机制 通过建立一个详细的模型来预测药物如何不同地改变血管功能， 这些群体之间。这是通过比较男性和女性的血管平滑度来实现的。 肌肉细胞使用一系列最先进的技术，包括电生理学，Ca 2 + 成像、纳米级超分辨率显微镜和详细的计算机预测建模。的 这个补充项目的目标是提供可靠的、可再现的和可扩展的分析 软件将统一这些固有的多模态数据集。这些分析管道的输出 将被用作计算模型的输入，以确保关键的可靠的地面实况预测 生物物理参数每个软件组件都将有一个完整的文档后端引擎 （用Python编写），具有用于互操作性的高级编程接口（API）， 可扩展性和被他人采用。基于云的计算和图形用户界面 将开发（GUI）。这些分析管道将通过使用工程来硬化 版本控制、单元测试、代码文档以及与现有 软件为了让其他人可以访问和使用这些工具，我们将使用商定的文件格式 和生物命名标准。为了使这些管道可拆卸，它们将 可通过多种渠道访问，包括在线开源代码共享和安装 包管理器。为了确保其他研究人员一键访问，管道将 容器化，安装方便可靠，允许相同的代码在单个 计算机、本地集群或云中。通过遵循最佳软件工程原则，我们的目标是 创建一个多模式分析管道，无缝地促进团队之间的协作 研究人员，并允许共享和发布数据和分析与更大的科学 社会，最终是公众。