All Outputs (4)

Rapid characterisation of hERG channel kinetics II: temperature dependence (2019)
Journal Article
Lei, C. L., Clerx, M., Beattie, K. A., Melgari, D., Hancox, J. C., Gavaghan, D. J., …Mirams, G. R. (2019). Rapid characterisation of hERG channel kinetics II: temperature dependence. Biophysical Journal, 117(12), 2455-2470. https://doi.org/10.1101/609719

Ion channel behavior can depend strongly on temperature, with faster kinetics at physiological temperatures leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-dependent ion channel... Read More about Rapid characterisation of hERG channel kinetics II: temperature dependence.

Four Ways to Fit an Ion Channel Model (2019)
Journal Article
Clerx, M., Beattie, K., Gavaghan, D., & Mirams, G. (2019). Four Ways to Fit an Ion Channel Model. Biophysical Journal, 117(12), 2420-2437. https://doi.org/10.1016/j.bpj.2019.08.001

© 2019 Biophysical Society Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict... Read More about Four Ways to Fit an Ion Channel Model.

Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System (2019)
Journal Article
Lei, C. L., Clerx, M., Gavaghan, D. J., Polonchuk, L., Mirams, G. R., & Wang, K. (2019). Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System. Biophysical Journal, 117(12), 2438-2454. https://doi.org/10.1016/j.bpj.2019.07.029

Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug-development, and requires a deep understanding of a compound’s action on ion channels. In vitro hERG-channel current recordings are an important step in evaluating the p... Read More about Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System.

Probabilistic Inference on Noisy Time Series (PINTS) (2019)
Journal Article
Clerx, M., Robinson, M., Lambert, B., Lei, C. L., Ghosh, S., Mirams, G. R., & Gavaghan, D. J. (2019). Probabilistic Inference on Noisy Time Series (PINTS). Journal of Open Research Software, 7(1), 23. https://doi.org/10.5334/jors.252

Time series models are ubiquitous in science, arising in any situation where researchers seek to understand how a system’s behaviour changes over time. A key problem in time series modelling is inference; determining properties of the underlying syst... Read More about Probabilistic Inference on Noisy Time Series (PINTS).