# Multi-Scale Modeling of Vascular Signaling Units

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $227,336

## Abstract

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.

## Key facts

- **NIH application ID:** 10406687
- **Project number:** 3R01HL152681-02S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** COLLEEN E CLANCY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $227,336
- **Award type:** 3
- **Project period:** 2021-09-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406687

## Citation

> US National Institutes of Health, RePORTER application 10406687, Multi-Scale Modeling of Vascular Signaling Units (3R01HL152681-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10406687. Licensed CC0.

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