Michael Clerx

This paper reviews and visualizes variability in reported midpoints of activation and inactivation of cardiac INa, finding large between-experiment variation and classifying contributing factors.

This paper compares in silico predictions of action potential duration changes with new human ex vivo recordings under IKr and ICaL inhibition, finding that no model accurately predicts all experimental outcomes.

The paper investigates deriving action potential biomarkers from a linearised action potential function.

This paper introduces an optimal experimental design for ion channel gating characterization by efficiently exploring the phase-voltage space using hERG/Kv11.1 currents.

This paper develops a computational model to identify and correct artefacts in voltage-clamp experiments, improving the accuracy of ion channel recordings.

This paper develops a method to automatically design optimal experimental protocols for calibrating cardiac electrophysiology models to better identify cell-specific parameters.

The paper shows that model selection based on AIC or model averaging can lead to unreliable treatment response predictions due to nonlinear dynamics, and proposes probabilistic averaging as a more reliable approach.

The paper describes extensions to an online resource for cardiac electrophysiology models, focusing on reproducible model development from experimental data.