Mechanochemical modeling of exercise-induced skeletal muscle hypertrophy

ABSTRACT Skeletal muscle displays remarkable plasticity, adapting its size and strength in response to mechanical loading, particularly, from exercise. This process, known as hypertrophy, is fundamental to athletic training and rehabilitation, but is challenging to quantitatively predict due to its multifactorial, multiscale nature. Specifically, skeletal muscle hypertrophy results from an integration of macroscopic mechanical stimuli with the intracellular signaling pathways that govern muscle growth. In this work, we present a multiscale computational model that mechanistically integrates these mechanical and biochemical stimuli and offers a framework for predicting the outcomes of different types of exercise on skeletal muscle growth. The framework couples a transversely isotropic hyperelastic model for tissue-level mechanics with a system of ordinary differential equations representing the IGF1-AKT-mTOR-FOXO signaling pathway, a key regulator of protein synthesis and degradation. We link these scales using a volumetric growth model, where the signaling dynamics inform a growth tensor that drives changes in muscle cross-sectional area. This approach enables the simulation of long-term muscle adaptation, providing a mechanistic tool to investigate how different exercise protocols lead to macroscopic hypertrophy. Simulations from our model capture the temporal dynamics of hypertrophy under varying load protocols and highlight how feedback between protein synthesis and muscle growth regulates the dose-response relationship to prevent unbounded growth. Using muscle geometries derived from the Visible Human dataset, we study how human variations in muscle geometry affect hypertrophy. Finally, we demonstrate that the mechanochemical coupling between muscle geometry and signaling not only predicts macroscopic shape changes but also provides buffering from local signaling heterogeneity. Ultimately, this framework offers a predictive computational tool for optimizing training regimens and understanding the multiscale determinants of muscle adaptations.

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Publication Details

Journal
PLoS Computational Biology
Published
2026-08-24
DOI
https://doi.org/10.1371/journal.pcbi.1014691
Primary Topic
Muscle Physiology and Disorders
Type
article
Field-Weighted Citation Impact
0.00

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article

Mechanochemical modeling of exercise-induced skeletal muscle hypertrophy

Marie E. Rognes, Ingvild S. Devold, Padmini Rangamani
PLoS Computational Biology
Muscle Physiology and Disorders
article

Mechanochemical modeling of exercise-induced skeletal muscle hypertrophy

Marie E. Rognes, Ingvild S. Devold, Padmini Rangamani
article en

Abstract

ABSTRACT Skeletal muscle displays remarkable plasticity, adapting its size and strength in response to mechanical loading, particularly, from exercise. This process, known as hypertrophy, is fundamental to athletic training and rehabilitation, but is challenging to quantitatively predict due to its multifactorial, multiscale nature. Specifically, skeletal muscle hypertrophy results from an integration of macroscopic mechanical stimuli with the intracellular signaling pathways that govern muscle growth. In this work, we present a multiscale computational model that mechanistically integrates these mechanical and biochemical stimuli and offers a framework for predicting the outcomes of different types of exercise on skeletal muscle growth. The framework couples a transversely isotropic hyperelastic model for tissue-level mechanics with a system of ordinary differential equations representing the IGF1-AKT-mTOR-FOXO signaling pathway, a key regulator of protein synthesis and degradation. We link these scales using a volumetric growth model, where the signaling dynamics inform a growth tensor that drives changes in muscle cross-sectional area. This approach enables the simulation of long-term muscle adaptation, providing a mechanistic tool to investigate how different exercise protocols lead to macroscopic hypertrophy. Simulations from our model capture the temporal dynamics of hypertrophy under varying load protocols and highlight how feedback between protein synthesis and muscle growth regulates the dose-response relationship to prevent unbounded growth. Using muscle geometries derived from the Visible Human dataset, we study how human variations in muscle geometry affect hypertrophy. Finally, we demonstrate that the mechanochemical coupling between muscle geometry and signaling not only predicts macroscopic shape changes but also provides buffering from local signaling heterogeneity. Ultimately, this framework offers a predictive computational tool for optimizing training regimens and understanding the multiscale determinants of muscle adaptations.

PLoS Computational BiologyVol. 22(8)
Simula Research Laboratory (NO), University of California San Diego (US), Norwegian Biochemical (Norway) (NO)
Stiftelsen Kristian Gerhard Jebsen, Universitetet i Oslo, Utdannings- og forskningsdepartementet, University of California, San Diego
Openalex Percentile: Top 99%
Muscle Physiology and Disorders
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