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Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics (2023)
Journal Article
Shuttleworth, J. G., Lei, C. L., Whittaker, D. G., Windley, M. J., Hill, A. P., Preston, S. P., & Mirams, G. R. (2024). Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics. Bulletin of Mathematical Biology, 86(1), Article 2. https://doi.org/10.1007/s11538-023-01224-6

When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological system... Read More about Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics.

The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk (2023)
Journal Article
Lei, C. L., Whittaker, D. G., & Mirams, G. R. (2024). The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk. British Journal of Pharmacology, 181(7), 987-1004. https://doi.org/10.1111/bph.16250

Background and Purpose
Drug-induced reduction of the rapid delayed rectifier potassium current carried by the human Ether-à-go-go-Related Gene (hERG) channel is associated with increased risk of arrhythmias. Recent updates to drug safety regulatory... Read More about The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk.

A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models (2022)
Journal Article
Barral, Y.-S. H. M., Shuttleworth, J., Clerx, M., Whittaker, D. G., Wang, K., Polonchuk, L., Gavaghan, D. J., & Mirams, G. R. (2022). A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models. Frontiers in Physiology, 13, Article 879035. https://doi.org/10.3389/fphys.2022.879035

Computational models of the electrical potential across a cell membrane are longstanding and vital tools in electrophysiology research and applications. These models describe how ionic currents, internal fluxes, and buffering interact to determine me... Read More about A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models.

A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability (2022)
Journal Article
Sher, A., Niederer, S. A., Mirams, G. R., Kirpichnikova, A., Allen, R., Pathmanathan, P., Gavaghan, D. J., van der Graaf, P. H., & Noble, D. (2022). A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability. Bulletin of Mathematical Biology, 84(3), Article 39. https://doi.org/10.1007/s11538-021-00982-5

There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models.... Read More about A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability.

Neural Network Differential Equations For Ion Channel Modelling (2021)
Journal Article
Lei, C. L., & Mirams, G. R. (2021). Neural Network Differential Equations For Ion Channel Modelling. Frontiers in Physiology, 12, Article 708944. https://doi.org/10.3389/fphys.2021.708944

Mathematical models of cardiac ion channels have been widely used to study and predict the behaviour of ion currents. Typically models are built using biophysically-based mechanistic principles such as Hodgkin-Huxley or Markov state transitions. Thes... Read More about Neural Network Differential Equations For Ion Channel Modelling.