Computational biomechanical modelling to predict musculoskeletal dynamics: application for 3Rs and changing muscle-bone dynamics

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This project has three goals: 1) to measure how muscles and bone adapt when a muscle/s are no longer able to function normally (e.g. injury). This will investigate the compensatory roles muscle develop in order to maintain functional movement, how their properties adapt to facilitate this movement, and how this affects bone growth; 2) to create and validate computational models that can predict how muscles and bone adapt when there is disruption to the "normal" functioning of the musculoskeletal system; 3) investigate the quantity of experimental input data required for the computational models to deliver accurate predictions.The outputs from this project will not only help researchers understand how the musculoskeletal system adapts to changes to "normal" function, but will also generate computational models that can replicate biomedical experiments that are frequently performed on animals. Such experiments are performed to test a range of things, such as the effects of disease/injury and biomedical devices on the musculoskeletal system. These experimentations, like many in musculoskeletal research, are highly invasive, and cause pain and distress to the animals before they are euthanized. Advances in computational modelling now enable models to predict how the body reacts to the dysfunctions of the musculoskeletal system caused by such experiments. Through replicating biomedical experiments, computational modelling has the potential to reduce, or even replace, the use of animals in musculoskeletal research and medical device design. The anatomy and behaviour of a computational model can be altered and re-tested without limitation to allow, for example: a model analysis to be extended to a different species by digital modification of the anatomy/behaviour; elements of anatomy to be modified in multiple ways (e.g. removal of muscle/bone) to examine the consequences of different surgical approaches; and for implant devices to be digitally inserted, all without the need for any harmful experimentation on real animals.The application of such computational modelling is still limited, so unfortunately a large number of animals are still used in biomedical experiments. There are many reasons for this, including the fact the building these models requires in-depth knowledge, and general scepticism that modelling can predict the outcomes of experiments with a high level of accuracy. We intend to address these issues by creating computational models of the rabbit that are validated against the form of experiments they are intended to reduce, or even replace. This validation requires a large amount of experimental data about how the rabbit bone and muscles adapt to dysfunctions of the musculoskeletal system. We will therefore collect detailed in vivo data on bone motion and muscle physiology at several time periods, to inform how rabbit bone and muscles adapt when there is alteration to the "normal" functioning of another muscle. This data will used to: 1) provide input data for the computational modelling; 2) determine the accuracy of the model predictions, thus determining the model validity.Rabbits have been chosen because they are widely used in a variety of research areas. They are the first-choice experimental animal for dental implant design and bone growth studies because of their size, easy handling and relative similarities to humans in terms of bone composition and healing. However, this project also has the potential to improve modelling of human biomechanics. Currently models are used widely to study healthy biomechanics (e.g. sports performance), ageing (e.g. sacropenia) and related diseases (e.g. osteoarithitis), dental procedures (e.g. orthodontic treatment) and injury (e.g. fracture). These human studies often estimate or predict parameters that cannot be measured directly in people, thus there is a clear need for accurate "off the self" computational models that we propose here.