Skip to main content
NC3Rs: National Centre for the Replacement Refinement & Reduction of Animals in Research
Project grant

Development of computational models of bone formation and resorption to predict changes in bone in preclinical intervention studies

A stock image of round glass dishes containing blue and green liquid arranged closely together.

At a glance

Award date
July 2013 - September 2015
Grant amount
Principal investigator
Dr Ilaria Bellantuono


University of Sheffield


  • Reduction
Read the abstract
View the grant profile on GtR

Application abstract

We will develop new computational models capable of predicting the extent and anatomical location of bone formation and/or resorption following bone anabolic interventions. We will use models of osteoporosis as proof of concept but the models developed will be applicable to any given intervention of genetic, metabolic, surgical, pharmacological, or functional nature. We will use a combination of in vitro data and data collected by the non-destructive in vivo micro computed tomography (microCT). This will allow the accurate three-dimensional measurement of bone tissue morphometry changes over time due to the intervention under investigation in the same region of the skeleton of the same animal.

Once validated, this quantitative information will be fed into a computer model that will determine the relationship between each specific intervention, the time, and the bone remodelling at each point of the bone. These models will then be used to make predictions on how the tissue morphology in a region of the mouse skeleton would change over a given time because of a given intervention. The accuracy of these predictions will be confirmed at the next time points, by simply comparing the computer predictions to the data generated by the in vivo microCT scanning.

The way in which candidate interventions are approved for human trials involves animal use for proof of principle studies in physiological models and uses large numbers of animals to generate statistically robust data at each time point. This technology will replace most of this in vivo testing and will require reduced number of animals to test at the chosen stratification due to the higher accuracy of the serial in vivo imaging technique. Moreover it will generate better quality data and allow a better understanding of the advantages and limitations of mouse models in testing interventions compared to human subjects.


  1. Cheong VS et al. (2019). A novel algorithm to predict bone changes in the mouse tibia properties under physiological conditions. Biomechanics and modeling in mechanobiology  19:985-1001. doi: 10.1007/s10237-019-01266-7
  2. Roberts BC et al. (2019). The longitudinal effects of ovariectomy on the morphometric, densitometric and mechanical properties in the murine tibia: A comparison between two mouse strains. Bone 127:260-270. doi: 10.1016/j.bone.2019.06.024
  3. Viceconti M et al. (2019). From bed to bench: How in silico medicine can help ageing research. Mechanisms of ageing and development 177:103-108. doi: 10.1016/j.mad.2018.07.001
  4. Zhang Y et al. (2019). A new method to monitor bone geometry changes at different spatial scales in the longitudinal in vivo μCT studies of mice bones. PLOS One 14(7):e0219404. doi: 10.1371/journal.pone.0219404
  5. Oliviero S et al. (2018). Validation of finite element models of the mouse tibia using digital volume correlation. Journal of the mechanical behavior of biomedical materials 86:172-184. doi: 10.1016/j.jmbbm.2018.06.022
  6. Dall'Ara E et al. (2017). Precision of Digital Volume Correlation Approaches for Strain Analysis in Bone Imaged with Micro-Computed Tomography at Different Dimensional Levels. Frontiers in Materials 4:31. doi: 10.3389/fmats.2017.00031
  7. Lu Y et al. (2017). Effect of integration time on the morphometric, densitometric and mechanical properties of the mouse tibia. Journal of biomechanics 65:203-11. doi: 10.1016/j.jbiomech.2017.10.026
  8. Lu Y et al. (2017). Longitudinal effects of Parathyroid Hormone treatment on morphological, densitometric and mechanical properties of mouse tibia. Journal of the mechanical behavior of biomedical materials 75:244-251. doi: 10.1016/j.jmbbm.2017.07.034
  9. Dall'Ara E et al. (2016). Longitudinal imaging of the ageing mouse. Mechanisms of ageing and development 160:93-116. doi: 10.1016/j.mad.2016.08.001
  10. Lu Y et al. (2016). Development of a protocol to quantify local bone adaptation over space and time: Quantification of reproducibility. Journal of biomechanics 49(10):2095-99. doi: 10.1016/j.jbiomech.2016.05.022
  11. Lu Y et al. (2015). Evaluation of in-vivo measurement errors associated with micro-computed tomography scans by means of the bone surface distance approach. Medical Engineering and Physics 37(11):1091-7. doi: 10.1016/j.medengphy.2015.08.017