# A computational model of the G&R during atherosclerosis: Integrating mechanics and biology

> **NIH NIH R01** · UNIVERSITY OF TEXAS DALLAS · 2021 · $376,670

## Abstract

PROJECT SUMMARY / ABSTRACT:
 For over a century cardiovascular diseases have been the primary cause of death in the
United States. Therefore, improved tools to aid in diagnosis and prognosis of atherosclerosis
are needed. A step towards this goal is to evaluate the hypothesis that integrative multi-scale,
multiphysics modeling is capable of predicting the growth and remodeling of atherogenesis in a
simulated coronary artery. The methods required to accomplish this objective will involve 1)
setting up a theoretical framework for a multiscale model capable of robustly integrating
interactions from the protein to tissue level of a coronary artery, 2) establishing an accurate
process to calculate the stresses resulting from pressurization and flow in this artery, 3)
coupling these simulations to create a congruent multiscale model capable of simulating
atherosclerotic plaque progression, and 4) validating model predictions to longitudinal in-vivo
human and ex-vivo porcine data detailing plaque progression. Previously, we have shown that
inclusive computational models are capable of predicting the hemodynamically, anatomically,
and mechano-chemo-biologically varying aspects during arterial remodeling under healthy and
hypertensive conditions. Since, this model was incapable of predicting the 3D changes,
atherosclerotic plaques, and mechanical inhomogeneity present in the advanced stages of
atherosclerosis, we present an approach combining agent based modeling (ABM) with finite
element analysis (FEA) and computational fluid dynamics (CFD) to create a modeling tool that
can predict the evolution of atherosclerotic plaque progression and instability. Together this
model will be able to handle mechano-geometric complexity (FEA & CFD) and chemo-
biological complexity (ABM) to a degree existing approaches cannot. From a basic science
perspective, by integrating numerous cell-level behaviors one can better understand the
underlying causes leading to plaque progression. Moreover, it will reveal areas that warrant
further research or reveal emergent properties otherwise overlooked. Ultimately, a better
multi-scale model of plaque evolution will be insightful for individualized decision making (e.g.
to treat or not to treat a lesion) and foundational for design changes in interventional
approaches (e.g. hypothesizing how an artery will respond to a pharmaceutical candidate, stent
design or graft).

## Key facts

- **NIH application ID:** 10084303
- **Project number:** 5R01HL136776-04
- **Recipient organization:** UNIVERSITY OF TEXAS DALLAS
- **Principal Investigator:** Heather Naomi Hayenga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $376,670
- **Award type:** 5
- **Project period:** 2018-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10084303

## Citation

> US National Institutes of Health, RePORTER application 10084303, A computational model of the G&R during atherosclerosis: Integrating mechanics and biology (5R01HL136776-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10084303. Licensed CC0.

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