(62ab) A Computational Investigation of Bone Biology | AIChE

(62ab) A Computational Investigation of Bone Biology


Cummings, P. T. - Presenter, Vanderbilt University
Pivonka, P. - Presenter, University of Western Australia
Jeon, J. - Presenter, Vanderbilt University
Buenzli, P. - Presenter, University of Western Australia

The bone cycle is a continuous and dynamic system in which old or damaged bone is constantly removed and replaced by new bone. This system consists of two basic cell types, osteoclasts and osteoblasts. The osteoclasts are responsible for the catabolic effect of bone resorption while the osteoblasts form bone, an anabolic process. The coordination between these two cell types is crucial in maintaining appropriate strength in one's bones. This system works in highly coordinated groups called basic multicellular units or BMUs. Utilizing various modeling techniques will lead to new clinical inquiries and treatments of diseases.

This work is a summary of my 3 month research scholarship visiting UWA. During that time I had the opportunity to work on a variety of problems related to bone regulation ranging from in-vitro to in-vivo applications. Despite the obvious and extensive self-regulation of bone tissue, biologists have been able to do little to understand the complexities of these cell-cell interactions. Several computational models attempt to shed light on these interactions, covering a large scope of biological queries related to the bone system. This presentation aims to highlight a few of these models and their potential applications. The first model will highlight the balance in differentiation of mesenchymal stem cells into osteoblasts versus adipocytes. The next model investigates the effects of parathyroid hormone on overall bone activity and resorption. Lastly, a spatial model of a single BMU demonstrates the spatial movement of these cells as they interact. The discussion will focus on the potential therapeutic applications of each model. Of particular interest will be the predictive nature of the models beyond that of traditional biological approaches.