Genomechanical modeling of delayed fracture healing integrating transcriptomics and tissue mechanics

Abstract

Fracture healing is a complex biological process that involves a coordinated interplay of immune responses, gene regulation, and mechanical forces. This study integrates advanced transcriptomic (RNA sequencing) and biomechanical modeling approaches to uncover the key molecular pathways and mechanical properties that drive bone repair. Using a rat femoral delayed fracture model, researchers analyzed gene expression changes, immune cell dynamics, and tissue mechanics at different healing stages. The findings reveal critical shifts in inflammation, cartilage formation, and bone remodeling, highlighting the role of signaling pathways such as Wnt and TGF-β in regulating these transitions.

Additionally, the study introduces a genomechanical (GM) model that incorporates gene expression data into predictive biomechanical simulations. This approach allows for a more accurate prediction of tissue differentiation and mechanical strength changes over time. The study demonstrates how genetic and mechanical factors work together to optimize healing and identifies potential therapeutic targets to improve fracture recovery, especially in conditions such as diabetes, aging, and obesity, where healing is impaired.

Importantly, this work introduces an integrative modeling framework that incorporates dynamic upstream regulator activity into a mechanoregulatory framework, enabling time-resolved simulation of gene-driven tissue transitions. The GM model provides a biologically informed platform for predicting healing trajectories and identifying optimal therapeutic windows, setting the stage for future applications in personalized and condition-specific treatment planning.

By bridging molecular biology with mechanical modeling, this research provides new insights into the biological mechanisms of bone repair, paving the way for personalized treatment strategies. The GM model offers a powerful tool for predicting healing outcomes and designing targeted interventions, ultimately improving patient care in orthopaedic medicine. These findings contribute to a growing body of knowledge that seeks to enhance fracture healing through precision medicine and advanced computational modeling.