BPS2026 - Integrating signaling and mechanics to predict exercise-induced muscle hypertrophy

Predicting skeletal muscle hypertrophy in response to exercise is challenging due to the complex interplay between tissue-level mechanics and cellular signaling. A critical gap exists in quantitatively connecting macroscopic mechanical loads to the molecular signaling cascades that regulate muscle growth. To bridge this gap, we present a multiscale computational model that mechanistically integrates these processes. The framework couples a transversely isotropic hyperelastic model for tissue mechanics with a system of ordinary differential equations describing the IGF1-AKT-mTOR-FOXO signaling pathway, a primary driver of protein synthesis and degradation. The link between scales is established through a volumetric growth model, where the simulated dynamics of the signaling network inform a growth tensor that drives macroscopic adaptation. The model also incorporates a feedback mechanism whereby hypertrophy downregulates the signaling pathway to limit further growth. Applying this framework to both idealized and anatomically realistic muscle geometries reveals that local growth patterns are strongly dependent on underlying tissue architecture. This integrated approach provides a computational tool for simulating long-term muscle adaptation, allowing for systematic investigation into how exercise regimens and muscle geometry interact to produce observable hypertrophy.