Our Research
Our main research theme aims at the investigation of causes and modulators of variability in the response of the heart to disease and therapies, using advanced computational modelling and simulation, signal processing and image analysis techniques in combination with experimental and clinical techniques. Below is a summary of our current research lines.
Safety and efficacy of drug therapy in human
We applied a Systems Medicine approach to the investigation of drug-induced effects on the human heart using computational modelling and simulation combined with experimental and clinical investigation. Specifically, we are interested in investigating the following Research questions.
Data-driven Modelling and Simulation for 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.
Non standard mathematical approaches for capturing variability
This strand of work attempts to underpin, using novel mathematical and computer software approaches, the other lines of research in the group.Mathematical modelling in Computational Cardiac Electrophysiology has a long history of over 50 years and can be broadly characterized by the modeling of ion flux dynamics in single cells, coupled, where appropriate, with models for the propagation of an action potential in tissue. The traditional approaches to this have been via ordinary differential equations and partial differential equations, based on the reaction diffusion equation, respectively. In the case of whole heart modeling the implementations often make use of high performance computing. Furthermore, these cell models are often highly tuned. These features of models being highly tuned and deterministic mean that it is very difficult to capture the underlying variability manifested by experiments and additionally that it is impossible to capture the stochasticity that is evident in all biological processes and that occurs at many different spatial and temporal scales.
Cardiovascular Imaging & Modelling
Imaging is a key component in assessment, treatment and follow-up of cardiovascular diseases. Our aim is to use cardiovascular images (echocardiography, cardiac MRI, hostology slices) to improve our understanding of healthy cardiac function and their alterations in different pathologies and different patients, and eventually to help in the development of patient-specific treatment. We are particularly focused on the study of the analysis of microstructure (myocyte orientations and their arrangement into sheets), from how to measure it (histology, in vitro and in vivo Diffusion MRI) to its effects on electrical and mechanical cardiac activity.
ECG biomarkers and Arrhythmic risk
How the degree and severity of different pathological ionic changes affect the ECG?The electrocardiographic signal (ECG) has been widely used over the last decades as a noninvasive tool to diagnose many cardiac diseases. The ECG is a realistic and personalized record of the electrical activity of the heart over time. Although different ECG features have been proposed to assess arrhythmic risk, they are not very specific as many of those are present in several disease conditions. The understanding of underlying ionic mechanisms at cellular level that expand to tissue, whole organ and eventually drive physiological ECG changes may improve the search of more specific ECG biomarkers. We are interested in searching novel ECG biomarkers that could prevent the occurrence of life threatening arrhythmias for different disease conditions.
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