These studies will provide a detailed characterization of the bone healing mechanical environment in vivo at both the tissue level and the local cell level. Additionally, the small scale and wireless properties of sensors advanced in this study will provide a platform to enable non-invasive quantification of the local mechanical environment for a broad range of regenerative engineering and medicine applications. Ultimately, these sensors could be utilized to transform the methods, modes, and costs of tissue replacements and enable the real time assessment of such cases as graft rejection and spinal fixation. This project will also combine an educational plan with three routes for broadening participation: (1) outreach programs in micro- and nanotechnology to K-12 participants; (2) GT-ENGAGE, a program which immerses high-school students from underrepresented groups in engineering research projects; and (3) the GT-SURE undergraduate research opportunity program. Through this approach, students across age ranges from K-college will be provided both a broad and detailed immersion in the research and technology development experience.
The research objective of this award is to develop a multi-scale mechanics approach to comprehensively characterize the in vivo mechanical environment within a tissue engineered construct during regeneration. Studies conducted under this award will utilize implantable wireless microelectromechanical (MEMS) sensors, biomechanical testing, and image-based finite element modeling. Tissue engineered constructs and dynamic fixation plates will be instrumented with wireless MEMS sensors; these will then be implemented in a well-established in vivo critically sized segmental bone defect model. In vivo measurements of the local strain gradients within the engineered constructs will be measured in real time during the course of regeneration to obtain a loading history. Image-based finite element models of the local mechanical strains in regenerating bone will be created and validated using the in vivo data from the implantable MEMS sensors.
