Anagrafe della ricerca

Muscular dystrophies (MDs) are a group of more than 30 genetic diseases characterised by progressive weakness and degeneration of the skeletal muscles that control movement. Amongst these, Duchenne muscular dystrophy (DMD) accounts for approximately 50% of cases, with an incidence of 1 out of every 5,000 live male births. Although numerous therapeutic approaches have been attempted to restore muscle function, there is currently no cure for MDs. Owing to species-specificity and inter-patient variability in disease onset and progression, along with the scarce availability of clinical data, an important source of support to biomedical research on MDs may come from mathematical models, which enable extrapolation beyond scenarios that can be investigated through experimental and clinical studies, thus offering the possibility to tease apart the molecular and cellular processes underlying this rare form of muscle disorders and facilitating the identification of new treatment possibilities. Yet, only few mathematical models for MDs have been developed to date and the existing models do not allow for systematic mathematical representation of the spatial and evolutionary cell dynamics that underpin the progression of MDs. Filling such a knowledge gap is a timely challenge, crucial for making progress in research areas where mathematical models offer innovative sustainable tools and digital solutions that can concretely help tackling major public health problems. The present project aims to fill this knowledge gap by establishing a novel unitary mathematical framework for modelling MD onset and progression, with particular focus on DMD. The models constituting such a framework, which will address limitations of existing mathematical models of MDs, will be calibrated and validated via the integration with clinical data from magnetic resonance imaging scans of MD patients. This will be accomplished through the development of novel computational methods for biomedical image segmentation and state-of-the-art uncertainty quantification methods to capture uncertainties linked to both clinical factors and possible bottlenecks in data acquisition processes. Undertaking the research activities underlying this project will require tackling a series of mathematical challenges, which will be overcome by harnessing the range of skills from across three teams of researchers with complementary mathematical expertise and know-how, who will work in close collaboration with biomedical experts in MDs and DMD. The application of the cutting-edge modelling approaches, analytical and statistical techniques, and numerical and computational methods that will be developed in this project will create interdisciplinary impact by advancing our theoretical understanding of MDs, informing new approaches for DMD treatment, and having the potential to open unexplored directions of mathematical modelling of other myopathies.