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Multi-scale models for the prediction of bone remodelling due to musculoskeletal interventions

Project Description

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. In silico approaches (computational models) have the potential of predicting bone changes over time due to local mechanical stimuli due to external loading and due to local biochemical stimuli due to changes in molecules that orchestrate the activity of the bone cells. Therefore, they can be used to predict the effect of osteoporosis and related treatments on the bone remodelling. Nevertheless, in order to account for both types of stimuli, complex multi-scale computational models based on high-resolution longitudinal imaging, finite element analyses (organ- and tissue-levels), and biological networks (cell-level) have to be developed and validated against experimental observations before their application in preclinical settings.

The hypothesis of this project is that multi-scale computational models can accurately predict bone changes over time due to osteoporosis and related interventions. 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 changes.

The student will first perform a literature review and will be trained to use the computational approaches available at the supervisors’ teams. Then they will develop of cell-level computational models for the prediction of the changes in molecular and cellular concentrations over time due to biochemical stimuli and will integrate it with already available validated finite element models for the prediction of local mechanical stimuli in the mouse tibia. The next part of the project will be to validate the models by comparing the outputs of the models with available experimental measurements performed with in vivo longitudinal microCT scans 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 changes due 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 of animals in research). For further details please contact the primary supervisor Dr Enrico Dall’Ara ().

Funding Notes

The Faculty of Medicine, Dentistry and Health have received one EPSRC studentship for 2020 entry from the Doctoral Training Partnership grant that is awarded to the University of Sheffield to fund PhD studentships in the EPSRC remit. This studentship will be 42 months in duration, and include home fee, stipend at UKRI rates and a research training support grant (RTSG) of £4,500.

Home/EU students must have spent the 3 years immediately preceding the start of their course in the UK to receive the full funding.


PLEASE NOTE: This project is also open to other schemes within the University of Sheffield, which are currently being advertised.

Candidates must have a first or upper second class honors degree or significant research experience. Degree in Engineering, Physics, Mathematics or equivalent.

Interested candidates should in the first instance contact Dr Enrico Dall-Ara ([email protected])

How to apply:
Please complete a University Postgraduate Research Application form available here:

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

Deadline for applications is 5pm on Wednesday 29th January 2020. Late applications will not be accepted. Interviews are scheduled to be held on Tuesday 25th February 2020.

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