Speakers
Description
Mathematical models are a key tool for describing physiological processes across multiple scales (organs, tissues, cells, molecules). Within the framework of precision medicine, differential models and their numerical (i.e. in silico) realizations can tackle complex biological phenomena with the goal of improving the treatment of human diseases. As a core component of digital twins, in silico models can indeed provide valuable support to biologists and clinicians in tasks such as designing biological experiments, predicting disease progression, and selecting drugs for novel therapies.
In this framework, advances in data analytics and cutting-edge HPC technologies, leading to ever increasing availability of large datasets and computational resources, can be leveraged to enhance and improve such models by integrating real data within a multiphysics approach where an increasingly important role is also played by artificial intelligence.
Here, we discuss current challenges and future perspectives involving different mathematical techniques applied to human diseases.
