Data-driven Modelling and Simulation in Cardiovascular Research
Due to the complex nature of cardiac electrophysiology, computational models represent the perfect tool to augment and fully analyse experimental and clinical findings. They allow for the integration of existing and new knowledge, enabling the investigation of cellular processes and cardiac arrhythmias with high spatio-temporal resolution. Such an approach involves a deep understanding on the nature of multiple data sources, from cellular ionic mechanisms and signaling pathways to electrical conduction at the tissue and whole organ levels. However, the construction of such multiscale models is subject to a number of challenging research questions:
- How can we link these types of data to mathematical modelling approaches, when most of the quantities of interest are not observable in the experimental or clinical practice?
- Which hypotheses are reasonable to capture and explain a given dataset?
- When traditional modelling techniques fail to explain important properties of the data, how do we embed novel mathematical ideas within cardiovascular research?
In order to answer these questions and to further advance our current understanding of heart function, our methodology is based on a synergistic clinical, experimental and computational approach, aiming to develop novel clinical relevant hypotheses, therapeutic targets, and technologies to improve risk identification under different disease conditions.