A biochemo-mechano multi-scale computational model to predict bone adaptation over space and time

Musculoskeletal diseases as osteoporosis have huge impact on the mortality and morbidity of our ageing society. At the moment there are some pharmacological interventions for treating osteoporosis but they are not effective in all patients and their cost is very high. New interventions have to be tested in animal models before clinical studies, the mouse being one of the most used models. Nevertheless, animal alternatives such as advanced in vitro (e.g. cell cultures, organ on a chip methods) and in silico approaches (i.e. computational modelling and digital twins) can improve the design and testing of new interventions and partially replace animal experimentation. Bone adapts over time and space thanks to the activity of the bone cells, which are triggered by biomechanical (e.g. mechanical loading or disuse) and biochemical (e.g. diseases or pharmacological treatments) stimulation. Therefore, the bone adaptation process is very complex as it involves different dimensional scales: the mechanical loading the bone is subjected to due to external forces (body level), the deformation the bone is subjected to under those forces (organ-tissue level) and the mechanical and biochemical stimuli that the cells feel (cell levels).

The Finite Element approach based on biomedical images can be used to estimate accurately how bone deforms under external loads [1]. Biological networks based on Ordinary Differential Equations have been used to estimate how biochemical stimuli affects bone adaptation [2]. Nevertheless, these two approaches have not yet been combined into a multi-scale model to predict bone adaptation over time. This is due to the fact that it is challenging to combine these two approaches and it is even harder to validate the outputs of the models (i.e. compare with experimental data that measure the bone adaptation over time). In our groups we have collected longitudinal high-resolution images of bone adaptation over time in a mouse model treated with biomechanical and/or pharmacological interventions [3], providing the best validation datasets for the validation of the multi-scale models. Moreover, we have shown that mechano-regulation models (i.e. models that consider explicitly only biomechanical stimuli) can predict reasonably well only bone adaptation [4]. The hypothesis of this project is that multi-scale computational models that account for both biomechanical and biochemical stimuli can accurately predict bone changes over space and time due to different treatments. The project aims at developing the first multi-scale biomechanical model for the prediction of bone changes over time in the mouse tibia due to external biomechanical and biochemical stimuli, and at validating its outcomes versus state-of-the-art longitudinal micro-computed tomography (microCT) measurements of bone adaptation. The student will first perform a literature review and will be trained to use the finite element modelling approaches available at the supervisors’ teams. Then they will develop cell-level biological networks for the prediction of the changes in molecular and cellular concentrations over time due to biochemical stimuli and will integrate them with the finite element models. They will perform model verification and sensitivity analysis to identify the most important sensitive input parameters in the models. They will validate the models versus experimental measurements performed with in vivo longitudinal microCT imaging of the mouse tibia, in mice treated with pharmacological and/or biomechanical interventions. The student will evaluate the importance of accounting for mechanical and/or biochemical stimuli for the accurate prediction of bone changesdue to interventions. Finally, the student will focus on scientific publications and writing the thesis. The project will generate academic impact (using the model to test new hypothesis and the effect of combined treatments), industrial impact (for companies that develop treatments for the musculoskeletal system), and 3Rs impact (reduction and partial replacement of the usage ofanimals in research).

How to apply:

Please complete a University Postgraduate Research Application form available here: http://www.shef.ac.uk/postgraduate/research/apply.

Please clearly state the prospective main supervisor in the respective box and select SMPH Oncology and Metabolism as the department.