An innovative modelling approach to estimating loading history from bone micro-architecture

Bone is remarkable tissue in that its morphology can inform us about the loading history in which it was developed. The fact that bone adapts in response to habitual mechanical stimuli is of interest to a wide range of research fields. Computational tools, such as Finite Element (FE) modelling and machine learning, have been applied in the clinical and paleo fields to study bone remodelling and its adaptation to function. Yet, with the amount of fossil data available in museum collections, it would be truly impactful to apply concepts of functional bone adaptation to uncover the behaviour of extinct species. Here, I propose a project that will explore the possibilities of developing an innovative modelling workflow to estimate loading history from bone morphology. The project will make optimal use of a newly established collaboration between KU Leuven, the Auckland Bioengineering Institute (ABI), and Penn State. We have access to a comprehensive dataset of distinct loading cases in the bipedal avian model of guineafowl, unique to the current research at Penn State. These will allow us to validate high fidelity (µ)FE modelling paradigms that will be developed within the world-leading biomechanical environment of the ABI. The developed workflows will be published Open Source to facilitate data sharing and interdisciplinary collaboration. As such, in this application I propose a well-rounded project that endeavours high-impact innovation and dependable risk management.