A computational model for prediction of morphology, patterning, and strength in bone regeneration - Project Summary/Abstract
Research on limb regeneration processes is growing at rapid pace. Evaluation of regenerative outcomes has
primarily focused on quantity, but investigation into resulting structural quality and function of the regenerated
tissue structure is critically lacking. New tools are needed to assess regenerative outcomes and treatments,
especially with regard to the structural quality and function of regenerated bone. Developing new tools to better
evaluate and understand complex limb regeneration processes directly serves to advance the ability to
regenerate human limbs using emerging technology, to improve the quality of life of babies and children with
limb defects, and to enable improved prosthesis performance. Small animal models are essential to the
development of regenerative treatments targeting injured bone and the translational pipeline for human use. It is
essential to develop computational tools to improve biomechanical evaluation of regenerated bone, and to
spatially understand where and when bone formation is occurring and how it can be strengthened. Our data
show that the mouse digit amputation model provides a highly reproducible model of bone regeneration after
amputation. Regeneration in this model can be tracked with repeated, in vivo, high resolution micro-computed
topography (µCT) scans over time, providing uniquely valuable data that is needed to construct a multiscale
finite element (FE) model of the digit. Further, we found that we are able to incorporate spatially localized
mechanical properties such as Young’s modulus computed from µCT volumetric density data. Building on this
we will develop a µCT to FE computational approach with integrated stochastic growth algorithms to
spatiotemporally simulate the morphological patterning and outgrowth of regenerated bone. We will validate
simulated growth using actual regenerative outcomes as measured by µCT data, and then apply our whole
approach to predict and test whole-bone digit strength and phenomenological reinjury outcomes. We will
compare computational results against physically tested bone, and identify specific areas of the bone that may
be strengthened to prevent reinjury through fracture. We hypothesize that our FE model will be able to spatially
predict bone formation patterning, whole bone strength, and phenomenological reinjury behavior based on input
parameters linked to initial morphology and regeneration processes. The proposed project will improve the rigor
of the digit regeneration model in the mouse and improve our understanding of limb regeneration treatment
outcomes and methods.
