# Multi-Scale Modeling to Predict Long-Term Growth and Remodeling of Skin in Response to Stretch

> **NIH NIH R01** · PURDUE UNIVERSITY · 2022 · $485,081

## Abstract

Project Summary
Breast cancer affects 1 in 8 women over their lifetime, and is the second most common cancer in women. Tissue
expansion (TE) is the most common technique for breast reconstruction after mastectomy. Unfortunately, the
rate of complications with TE for breast reconstruction can be 15% or higher in large series, not including poor
cosmetic and the negative impact to body image. Growing skin in shape and amount adequate to achieve a
natural breast shape is a key need, as successful reconstruction has been shown to markedly improve survivors’
quality of life. TE is challenging in areas with complex three-dimensional (3D) geometries, which show unequal
stretch and growth distributions that cannot be currently anticipated. We have pioneered the application of finite
element tools to TE, as well as a novel experimental protocol in the swine that has allowed us to measure, for
the first time, tissue scale prestrain, deformation induced by expansion, and resulting growth in realistic TE
protocols. The porcine model has confirmed predictions made with our computational model, that the
deformation is heterogeneous, with the apex of the expander undergoing the largest strains, and that the growth
patterns reflect the deformation contours. Here we will leverage our unique animal model to measure accurately
the tissue scale deformation and growth, together with the corresponding cell behavior and microstructure
remodeling at specific time points during TE. This information will provide the first complete picture of chronic
skin adaptation to mechanical cues across scales. The data will allow us to improve our previous computational
model, and create, calibrate and validate a new multi-scale model. Specifically, we will predict microscopic
remodeling as a function of cell behavior in response to stretch (Aim 1), predict skin growth during tissue
expansion using a new organ-scale model (Aim 2), and translate the experiment and model to the clinical setting
of breast reconstruction after mastectomy to predict the growth of human skin as a function of inflation timing
and volume (Aim 3). This project will thus add to the fundamental knowledge of skin biology, help improve clinical
outcomes and provide topics for further research into therapeutic intervention.

## Key facts

- **NIH application ID:** 10416016
- **Project number:** 5R01AR074525-04
- **Recipient organization:** PURDUE UNIVERSITY
- **Principal Investigator:** Adrian Buganza Tepole
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $485,081
- **Award type:** 5
- **Project period:** 2019-07-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10416016

## Citation

> US National Institutes of Health, RePORTER application 10416016, Multi-Scale Modeling to Predict Long-Term Growth and Remodeling of Skin in Response to Stretch (5R01AR074525-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10416016. Licensed CC0.

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