All Outputs (9)

Optimising experimental designs for model selection of ion channel drug binding mechanisms (2024)
Preprint / Working Paper
Patten-Elliott, F., Lei, C. L., Preston, S. P., Wilkinson, R. D., & Mirams, G. R. Optimising experimental designs for model selection of ion channel drug binding mechanisms

The rapid delayed rectifier current carried by the human Ether-à-go-go-Related Gene (hERG) channel is susceptible to drug-induced reduction which can lead to an increased risk of cardiac arrhythmia. Establishing the mechanism by which a specific drug... Read More about Optimising experimental designs for model selection of ion channel drug binding mechanisms.

Reconstructing the Antarctic ice sheet shape at the Last Glacial Maximum using ice core data (2023)
Journal Article
Turner, F. E., Buck, C. E., Jones, J. M., Sime, L., Vallet, I. M., & Wilkinson, R. D. (2023). Reconstructing the Antarctic ice sheet shape at the Last Glacial Maximum using ice core data. Journal of the Royal Statistical Society: Series C, 72(5), 1493-1511. https://doi.org/10.1093/jrsssc/qlad078

The Antarctic ice sheet (AIS) is the Earth's largest store of frozen water; understanding how it has changed in the past allows us to improve our future projections of how it, and thus sea levels, may change. In this paper, we use previous reconstruc... Read More about Reconstructing the Antarctic ice sheet shape at the Last Glacial Maximum using ice core data.

Modelling calibration uncertainty in networks of environmental sensors (2023)
Journal Article
Smith, M. T., Ross, M., Ssematimba, J., Álvarez, M. A., Bainomugisha, E., & Wilkinson, R. (2023). Modelling calibration uncertainty in networks of environmental sensors. Journal of the Royal Statistical Society: Series C, 72(5), 1187-1209. https://doi.org/10.1093/jrsssc/qlad075

Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can b... Read More about Modelling calibration uncertainty in networks of environmental sensors.

Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators (2023)
Journal Article
Strocchi, M., Longobardi, S., Augustin, C. M., Gsell, M. A. F., Petras, A., Rinaldi, C. A., …Niederer, S. A. (2023). Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators. PLoS Computational Biology, 19(6), Article e1011257. https://doi.org/10.1371/journal.pcbi.1011257

Cardiac pump function arises from a series of highly orchestrated events across multiple scales. Computational electromechanics can encode these events in physics-constrained models. However, the large number of parameters in these models has made th... Read More about Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.

Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds (2022)
Journal Article
Coveney, S., Roney, C. H., Corrado, C., Wilkinson, R. D., Oakley, J. E., Niederer, S. A., & Clayton, R. H. (2022). Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds. Scientific Reports, 12, Article 16572. https://doi.org/10.1038/s41598-022-20745-z

Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models fr... Read More about Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators (2021)
Journal Article
Coveney, S., Corrado, C., Oakley, J. E., Wilkinson, R. D., Niederer, S. A., & Clayton, R. H. (2021). Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Frontiers in Physiology, 12, Article 693015. https://doi.org/10.3389/fphys.2021.693015

Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into c... Read More about Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators.

FDCCS16 molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS (2016)
Journal Article
Cresswell, A. J., Wheatley, R. J., Wilkinson, R. D., & Graham, R. S. (in press). FDCCS16 molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS. Faraday Discussions, 192, https://doi.org/10.1039/C6FD00026F

Impurities from the CCS chain can greatly influence the physical properties of CO2. This has important design, safety and cost implications for the compression, transport and storage of CO2. There is an urgent need to understand and predict the prope... Read More about FDCCS16 molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS.

Quantifying simulator discrepancy in discrete-time dynamical simulators (2011)
Journal Article
Wilkinson, R. D., Vrettas, M., Cornford, D., & Oakley, J. E. Quantifying simulator discrepancy in discrete-time dynamical simulators. Manuscript submitted for publication

When making predictions with complex simulators it can be important to quantify the various sources of uncertainty. Errors in the structural specification of the simulator, for example due to missing processes or incorrect mathematical specification... Read More about Quantifying simulator discrepancy in discrete-time dynamical simulators.

Adjoint-aided inference of Gaussian process driven differential equations
Presentation / Conference Contribution
Gahungu, P., Lanyon, C. W., Álvarez, M. A., Smith, M. T., & Wilkinson, R. D. (2022, November). Adjoint-aided inference of Gaussian process driven differential equations. Presented at NeurIPS 2022: Thirty-sixth Conference on Neural Information Processing Systems, New Orleans, USA and online

Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lies in using noisy observations of the system to infer the forcing, as w... Read More about Adjoint-aided inference of Gaussian process driven differential equations.