The research objective of this award is to develop a novel computational framework for predicting the adaptation of skeletal muscle to changes in mechanical environment, as occurring after surgical procedures. Current computational models are limited in their capacity to predict long-term muscle adaptation because they lack a comprehensive formulation of the underlying mechanobiological response mechanisms. Under this award, a novel computational strategy will be developed to quantify the remodeling of muscle-tendon morphology and function in response to surgically-induced mechanical changes by integrating finite-element (FE) modeling and agent-based (AB) modeling approaches. The computational strategy makes use of a practical multi-scale modeling workflow that explicitly builds on a series of experiments that characterize the response of muscle tissue in a rabbit model of muscle-tendon transfer surgery.
If successful, these studies will add significantly to the understanding of the feedback between mechanical stimuli and physiological processes that determines skeletal muscle adaptation. Once developed, the models created under this award can be applied to study a wide range of remodeling and adaptation responses of muscle to loading, unloading, and disease, enabling new hypotheses that will advance current research of musculoskeletal biology and mechanics. Similarly, once fine-tuned to a particular surgical model, the computational models will ultimately inform surgeons of the most effective approach, which will improve the outcomes of surgeries, leading to better quality of life for patients. As part of the research program, k-12 teachers will be hosted in the PIs? research laboratories over the summer. The PIs, teachers, and students will collaborate on teaching kits that will be delivered to the teachers? schools and published on TeachEngineering.com so that they are available for the general population.
