Colleen E. Clancy

This paper develops a computational model linking mitochondrial ROS, calcium, and electrophysiology in human ventricular myocytes, showing how ROS and calcium interact to influence cardiac function and arrhythmia risk.

This paper develops a new methodology to model phosphoinositide signaling dynamics, showing that basal PI(4)P levels determine cell-specific responses to genetic perturbations.

The paper investigates how hERG blockers like nifekalant can increase K+ channel current through an agonism-while-blocking mechanism, potentially explaining their safety profile.

The paper uses synthetic data to create digital twins of human cardiomyocytes, enabling precise modeling of electrophysiological properties from experimental recordings.

This paper uses AlphaFold to predict discrete conformational states of hERG channels, revealing structural features and drug interactions that improve drug safety screening.

The paper develops structural models of beta-1 and beta-2 adrenergic receptors with beta-blockers using RosettaLigand and MD simulations, identifying structural features and stability but not clearly distinguishing stereospecific drug preferences.

This paper develops a deep learning model to classify and predict electrophysiological responses in cardiac cells across developmental stages using simulated and experimental data.

This paper develops a multiscale computational model to predict cardiotoxicity from drug chemistry to cardiac rhythm, validated using clinical data and showing high predictive accuracy for arrhythmia vulnerability.