# Modeling to design optimized estrogen-specific muscle regeneration treatment

> **NIH NIH R21** · UNIVERSITY OF VIRGINIA · 2022 · $202,432

## Abstract

SUMMARY
The goal of this proposal is to develop an estrogen-specific muscle regeneration agent-based model (ABM) and
use it to conduct in silico experiments to identify the optimal set of growth factor interventions that improve muscle
regeneration following injury. This new model will allow the field to understand how sex differences play a role
in the treatment of muscle injury. It is known that current muscle injury treatments cleared by the FDA have sex-
differences, which can be attributed to differences in sex hormones. Likewise, it is well documented that estrogen
has a multi-faceted impact on muscle regeneration. However, while muscle regeneration models have been
developed, they are based on male-only data and do not offer any insight into how sex differences alter muscle
injury outcomes. These profound limitations leave the field without any tools to examine how differences in sex
hormones may influence muscle damage, regeneration, and treatment outcomes.
 This project has two key aims that will resolve these profound limitations. The first aim will develop and
validate a muscle regeneration ABM that accounts for the role of estrogen through the use of coupled in vivo
and in silico experiments. This aim will be achieved by collecting data from freeze injured female mice that are
receiving specified amounts of estrogen and using different subsets of the data to tune and validate the ABM.
The second aim will simulate combinations of growth factors at varying levels of estrogen to identify how
treatment procedures could be optimized by timing the dosage of growth factors according to estrogen levels
during the menstrual cycle. Reinforcement machine learning will be used to identify the combination of growth
factors, dosages, and timing that would lead to the fastest muscle recovery at varying levels of estrogen. These
model-predicted optimal treatments for each estrogen level will be experimentally tested in vivo. Taken together,
these aims will develop the first computational model that incorporates the effects of estrogen levels to study
how they impact muscle injury treatments. This work will provide an important new understanding of sex-based
differences in muscle damage, inflammation, satellite stem cell response, and overall muscle regeneration
outcomes that will be crucial to developing treatments that account for these differences.

## Key facts

- **NIH application ID:** 10363144
- **Project number:** 1R21AR080415-01
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Silvia Salinas Blemker
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $202,432
- **Award type:** 1
- **Project period:** 2022-02-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10363144

## Citation

> US National Institutes of Health, RePORTER application 10363144, Modeling to design optimized estrogen-specific muscle regeneration treatment (1R21AR080415-01). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10363144. Licensed CC0.

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