Programme theme
The Physiome Project aims to explain how each component of the human body operates as part of an integrated whole. Developing mathematical and computational models that represent interacting physical mechanisms and are informed by experimental and clinical data is a key component of this activity. Increasingly, this approach is moving from qualitative studies of laboratory experiments to clinical applications by providing quantitative predictions to aid diagnosis, facilitate drug and device development and guide treatment. Models of the heart have been a prominent feature of this enterprise, and it is now possible to construct models that represent the electromechanical behaviour of the heart of an individual patient. However, to be clinically useful, mathematical and computational models of the heart need to predict outcomes in a specific patient given available information. The inherent variability in patient physiology, pathology and therapies, combined with the sparsity and noise inherent in medical images and physiological data, necessitates quantification of uncertainty in these models to inform clinicians and decision makers of how much to rely on a prediction, to quantify confidence in model parameters, and to assist model developers in improving their predictions.
Models of the heart are typically systems of nonlinear ordinary differential equations and nonlinear systems of partial differential equations constrained by conservation laws. These are solved using numerical techniques, on a space scale from single cells up to meshes derived from medical images of the heart. Earlier work focused on developing efficient numerical methods and software solutions to reduce the time and computational cost of cardiac model simulations. Increasingly, with new software and hardware this is no longer the largest challenge. The latest major challenges facing cardiac modelling include parameter inference from uncertain experimental measurements, model personalisation to patient data, model selection, model discrepancy from reality, and most importantly how these factors affect the confidence in model predictions. To address these new challenges requires better links between statistics, mathematics and cardiac modelling communities to develop new mathematical techniques that are tailored to the challenges of patient-specific model development and its applications. This Isaac Newton Institute programme will bring together the statistics and cardiac modelling communities to identify challenging problems in cardiac modelling and novel mathematical and statistical solutions to these problems.
The Institute kindly requests that any papers published as a result of this programme’s activities are credited as such. Please acknowledge the support of the Institute in your paper using the following text:
The author(s) would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme The fickle heart, where work on this paper was undertaken. This work was supported by EPSRC grant EP/R014604/1.
Click here to download the programme's final scientific report
FHT programme: interview with organisers Steven Niederer and Richard Clayton
Workshops
Industrial and Clinical Application of Cardiac Simulations: Quantifying Uncertainty in Model Predictions
4 June 2019 to 4 June 2019
Uncertainty quantification for cardiac models
5 June 2019 to 7 June 2019
Fickle Heart: The intersection of UQ, AI and Digital Twins
3 June 2024 to 4 June 2024
Organisers
- Helen Byrne University of Oxford
- Richard Clayton University of Sheffield
- Gary Mirams University of Nottingham
- Steven Niederer King's College London
- Ian Vernon University of Durham
Participants
- Yasser Aboelkassem University of California, San Diego
- Kylie Beattie GlaxoSmithKline
- Helen Byrne University of Oxford
- Ben Calderhead Imperial College London
- Kenneth Campbell University of Kentucky
- Chris Cantwell Imperial College London
- Peter Challenor University of Exeter
- Elizabeth Cherry Rochester Institute of Technology
- David Christini Cornell University
- Richard Clayton University of Sheffield
- Cesare Corrado King's College London
- Samuel Coveney University of Sheffield
- Tammo Delhaas University of Maastricht
- Flavio Fenton Georgia Institute of Technology
- Sarah Filippi Imperial College London
- Eleanora Grandi University of California, Davis
- Wouter Huberts University of Maastricht; Technische Universiteit Eindhoven
- Chon Lok Lei University of Oxford
- Gary Mirams University of Nottingham
- Hossein Mohammadi EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter
- Haibo Ni University of California, Davis
- Steven Niederer King's College London
- Chris Oates Newcastle University
- Alexander Panfilov Universiteit Gent; Universiteit Leiden
- Pras Pathmanathan U. S. Food and Drug Administration (FDA)
- Gernot Plank Medizinische Universität Graz
- Catherine Powell University of Manchester
- Marina Riabiz King's College London; The Alan Turing Institute
- Caroline Roney King's College London
- Bruce Smaill University of Auckland
- Ian Vernon Durham University
- Linwei Wang Rochester Institute of Technology
- Rodrigo Weber dos Santos Universidade Federal de Juiz de Fora
- Dominic Whittaker University of Nottingham
- Richard Wilkinson University of Sheffield
- Keith Worden University of Sheffield
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