Predicting cortical bone adaptation to axial loading in the mouse tibia
- Subject Areas
- Bioengineering, Computational Biology, Orthopedics
- Bone, mechanobiology, fluid-flow, mouse, tibia, FEA
- © 2015 Pereira et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Predicting cortical bone adaptation to axial loading in the mouse tibia. PeerJ PrePrints 3:e1140v3 https://doi.org/10.7287/peerj.preprints.1140v3
The development of predictive mathematical models can contribute to a deeper understanding of the specific stages of bone mechanobiology and the process by which bone adapts to mechanical forces. The objective of this work was to predict, with spatial accuracy, cortical bone adaptation to mechanical load, in order to better understand the mechanical cues that might be driving adaptation.
The axial tibial loading model was used to trigger cortical bone adaptation in C57BL/6 mice and provide relevant biological and biomechanical information. A method for mapping cortical thickness in the mouse tibia diaphysis was developed, allowing for a thorough spatial description of where bone adaptation occurs.
Poroelastic finite-element (FE) models were used to determine the structural response of the tibia upon axial loading and interstitial fluid velocity as the mechanical stimulus. FE models were coupled with mechanobiological governing equations, which accounted for non-static loads and assumed that bone responds instantly to local mechanical cues in an on-off manner.
The presented formulation was able to simulate the areas of adaptation and accurately reproduce the distributions of cortical thickening observed in the experimental data with a statistically significant positive correlation (Kendall's tau rank coefficient \(\tau = 0.51\), \(p<0.001\)).
This work demonstrates that computational models can spatially predict cortical bone mechanoadaptation to time variant stimulus. Such models could be used in the design of more efficient loading protocols and drugs therapies that target the relevant physiological mechanisms.
Accepted version for the Journal of The Royal Society Interface