Elizabeth Cherry - Featured Faculty 2013
Elizabeth Cherry
College of Science
Elizabeth Cherry is an assistant professor in the School of Mathematical Sciences. Her research interests are centered on the study of cardiac arrhythmias using mathematical and computational techniques.
Sudden cardiac death resulting from disruptions to the heart's normal rhythm remains the leading cause of death in the industrialized world, causing approximately 20 percent of all deaths. Research has shown that the most dangerous cardiac arrhythmias arise from reentrant waves corresponding to spiral or scroll waves of electrical activity within the heart. Because the frequencies of these reentrant waves are higher than that of the heart's own pacemaker, the heart's natural rhythm is disturbed, triggering mechanical dysfunction that prevents adequate contraction and pumping of blood. Despite the obvious medical significance, much remains to be understood about the mechanisms responsible for the formation and evolution of arrhythmias in the human heart.
Cherry's research, which is supported by the National Science Foundation, is focused on improving the understanding of cardiac electrical dynamics and arrhythmias in normal and diseased states by using mathematical modeling and simulation. Her work integrates perspectives and techniques from mathematics, computer science, physics, engineering, and biology and encompasses everything from construction of mathematical models for describing cellular processes to efficient implementation of large-scale computer codes and testing arrhythmia mechanisms using that computational platform. Along with developing several new models and algorithms, she and her collaborators have developed a novel low-energy method for defibrillation.
Cherry and her collaborators are currently conducting research in several new areas, including creating systematic approaches to model development, identifying dangerous precursor states to arrhythmias that can allow early intervention, and using techniques from weather forecasting to integrate experimental observations into model predictions.