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

> **NIH NIH R01** · PURDUE UNIVERSITY · 2022 · $189,254

## Abstract

Project Summary
Mastectomy continues to be a standard of treatment for breast cancer, the most common cancer in women.
Tissue expansion (TE) is the preferred technique for breast reconstruction after mastectomy. Unfortunately,
the rate of complications with TE for breast reconstruction can be 15% or higher, not including poor cosmetic
outcomes. In the parent grant we are working with our proposed porcine model of TE, as well as our
computational modeling framework of skin growth. As part of the parent award, our work has shown that skin
growth rate is proportional to the amount of deformation, and that this process has a characteristic time
constant on the order of a few days. We have further looked into the cellular mechanisms that drive skin
growth and identified key mechanotranduction pathways that lead to increased cell proliferation. We have also
started collection of patient data to translate the findings from the porcine model to human patients. On the
other hand, machine learning (ML) has permeated engineering sciences, enabling analysis of biological
processes that would otherwise be impossible with traditional approaches. In particular, we have been at the
forefront of applying ML tools to our experimental data and computational models of skin growth in TE. In this
Supplement proposal we will further develop ML tools to identify the signaling network dynamics that best
explain the mechanisms by which cells adapt to mechanical cues (Aim S1); we will create image-registration
frameworks using physics-informed neural networks to process 3D images from the clinical setting in which
precise measurement of tissue deformation is challenging (Aim S2); and we will establish ML optimization
frameworks to design TE protocols that can lead to desired outcomes in terms of time and pattern of skin
growth (Aim S3). In parallel to the research objectives, this Supplement will establish a sequence of courses
and workshops to mentor senior graduate students and postdoctoral scholars and foster Diversity, Equity,
Inclusion and Accessibility (DEIA) at Purdue University.

## Key facts

- **NIH application ID:** 10605576
- **Project number:** 3R01AR074525-04S2
- **Recipient organization:** PURDUE UNIVERSITY
- **Principal Investigator:** Adrian Buganza Tepole
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $189,254
- **Award type:** 3
- **Project period:** 2019-07-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10605576

## Citation

> US National Institutes of Health, RePORTER application 10605576, Multi-Scale Modeling to Predict Long-Term Growth and Remodeling of Skin in Response to Stretch (3R01AR074525-04S2). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10605576. Licensed CC0.

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