# Multiscale Modeling of Aortic Homeostasis

> **NIH NIH R03** · YALE UNIVERSITY · 2021 · $83,750

## Abstract

PROJECT SUMMARY. Mechanical homeostasis is a process by which the vasculature adapts to changes in
blood flow, blood pressure, and other influences. Mounting evidence suggests that compromised or lost
homeostasis is a cause or consequence of many vascular diseases. There is, therefore, a pressing need for an
increased understanding of vascular homeostasis, which necessarily derives from molecular and cellular
processes but manifests at the tissue level via negative feedback that can be described mathematically.
The goal of this project is to use an existing extensive data set on aortic remodeling in a unique mouse model of
hypertension to inform and validate a new multiscale model of vascular homeostasis. Once achieved, such a
model promises to help delineate compensatory mechanisms that promote tissue homeostasis via changes in
cell signaling versus pathologic mechanisms that prevent homeostasis. Toward this end, we will meld recent
advances in cell signaling models and continuum level growth and remodeling models to describe and predict
data from a unique mouse model of hypertension wherein aortic remodeling is adaptive because of a preserved
contractile phenotype and augmented synthetic phenotype, with inherently low inflammation. In this way we will
avoid the typical complication of inflammation that is present in other mouse models of hypertension and drives
the response away from homeostasis. We will inform our mechanobiologically motivated multiscale model using
a combination of data from RNA sequencing, quantitative histology, and biaxial biomechanical (passive and
active) data. Importantly, this data-informed model will enable us to explore, for the first time, the potentially
adaptive versus maladaptive changes in cell signaling topology that promote or prevent effective homeostasis,
thus representing a paradigm shift in the way some vascular diseases are understood and how best to treat
them. Hypertension, for example, is rampant in this country and is a key risk factor for diverse cardiovascular,
neurovascular, and renovascular diseases. This work is significant biologically for it has potential to provide new
insight into this insidious risk factor. More generally, however, tissue homeostasis is fundamental to many
different tissues and organs and our general computational approach promises to be generally applicable.
Finally, this work is highly innovative for it will identify a new computational framework for integrating information
across scales from differentially expressed genes to tissue-level manifestations, and it will enable delineation of
potentially homeostatic versus non-homeostatic responses to diverse genetic mutations or small molecule
interventions, which can guide therapeutic intervention. This proposal is submitted via the R03 mechanism since
its focus is “Development of research methodology” and “Secondary analysis of existing data”, specifically, a
unique multiscale data set obtained from a fortuitously discovered...

## Key facts

- **NIH application ID:** 10189114
- **Project number:** 1R03EB031123-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Jay D. Humphrey
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $83,750
- **Award type:** 1
- **Project period:** 2021-08-19 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189114

## Citation

> US National Institutes of Health, RePORTER application 10189114, Multiscale Modeling of Aortic Homeostasis (1R03EB031123-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10189114. Licensed CC0.

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