Gary Richard Mirams

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Professor of Mathematical Physiology / Wellcome Trust Senior Research Fellow

University of Nottingham

United Kingdom

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About

Professor Gary Richard Mirams is a Professor of Mathematical Physiology and a Wellcome Trust Senior Research Fellow at the University of Nottingham, UK. His research areas include optimal experimental designs in ion channel gating, the development of effective voltage-clamp protocols, and the application of neural networks for pharmacological studies. He has contributed significantly to understanding the impact of modeling uncertainties in cardiac electrophysiology and drug-induced arrhythmia risk through his recent publications.

Recent Grants

Grant: Close

Developing cardiac electrophysiology models for drug safety studies

Open Date: 2019-02-01

Close Date: 2024-02-01

Grant: Close

Sinusoidal voltage protocols for characterisation of ion channel kinetics

Open Date: 2019-01-01

Close Date: 2021-12-31

Grant: Close

Reproducible development of mathematical models of cardiac electrophysiology

Open Date: 2017-05-01

Close Date: 2020-04-30

Grant: Close

Improving assessment of drug-induced cardiac risk with mathematical electrophysiology models.

Open Date: 2014-02-01

Close Date: 2019-01-31

Grant: Close

Prediction of human cardiotoxic QT prolongation using in-vitro multiple ion channel data and mathematical models of cardiac myocytes

Open Date: 2013-02-14

Close Date: 2014-04-12

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Collaborators (16)

Neil Herring

University of Oxford

UNITED KINGDOM

Gary Richard Mirams

Research Interests

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About

Recent Grants

Articles (20)

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies

Optimal experimental designs for characterising ion channel gating by filling the phase-voltage space of model dynamics

A range of voltage-clamp protocol designs for rapid capture of hERG kinetics

Ten simple rules for training scientists to make better software

An experimental investigation of rundown of the L-type calcium current

The impact of uncertainty in hERG binding mechanism on in silico predictions of drug‐induced proarrhythmic risk

Understanding the impact of numerical solvers on inference for differential equation models

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

Leak current, even with gigaohm seals, can cause misinterpretation of stem cell-derived cardiomyocyte action potential recordings

Model-driven optimal experimental design for calibrating cardiac electrophysiology models

chaste codegen: automatic CellML to C++ code generation with fixes for singularities and automatically generated Jacobians

Electrophysiological characterization of the hERG R56Q LQTS variant and targeted rescue by the activator RPR260243

Cardiac TdP risk stratification modelling of anti-infective compounds including chloroquine and hydroxychloroquine

Considering discrepancy when calibrating a mechanistic electrophysiology model

Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments

Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk

A nonlinear and time-dependent leak current in the presence of calcium fluoride patch-clamp seal enhancer

Calibration of ionic and cellular cardiac electrophysiology models

The fickle heart: uncertainty quantification in cardiac and cardiovascular modelling and simulation

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

Collaborators (16)

Neil Herring

Glen F. Tibbits

Teun de Boer

Gundolf Haase

david gavaghan

Richard Wilkinson

Michael Clerx

Maurice Hendrix

Wendy Wu

Jonathan Cooper

Rebecca-Ann Burton

Kit Gallagher

David J. Christini

Chris D. Cantwell

Marina Riabiz

Yasser Aboelkassem

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