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(Circulation Research. 2002;90:842.)
© 2002 American Heart Association, Inc.

Editorials

"Are We There Yet?!"

Cardiac Channelopathy and Our Journey Toward Computational Medicine

Reza Mazhari

From the Institute of Molecular Cardiobiology, Department of Medicine, Johns Hopkins University, Baltimore, Md.

Correspondence to Reza Mazhari, PhD, Institute of Molecular Cardiobiology, Johns Hopkins School of Medicine, 720 Rutland Ave, Ross Bldg 844, Baltimore, MD 21205. E-mail rmazhari@bme.jhu.edu

Key Words: electrophysiology • arrhythmia • mathematical model • long-QT syndrome • gene therapy

Half a century ago, Hodgkin and Huxley¹ developed an elegant theoretical model of excitability in nerves, which to date serves as the backbone for mathematical models representing the cardiac action potential. Evolution of such models, from the earliest effort of Noble in 1960,² to the recent biophysically detailed generation of models,^3–5 has been limited less by computational methods than by the state of the available biological data. Through technological advancements in patch-clamp and voltage-clamp techniques (both whole-cell and single-channel recordings), biophysical properties of different ion channels, contributing to different phases of the cardiac action potential, have been elucidated in the past 4 decades. Recent discoveries in genomics and proteomics have further propelled our understanding of channel structure and function^6–9 and have shed light on gating mechanisms and drug interactions. While early computational models were aimed at recapitulating the normal action potential using few ionic currents, the later generations consist of numerous channels, pumps, and exchangers (see Noble and Rudy¹⁰ for a historical perspective). Consequently, modern models allow us to pose complex hypotheses and to test these hypotheses in an integrative manner so as to probe the underlying mechanisms of the diseased state (Figure). It is important to emphasize the term "integrative," because often the course of action is an iterative process where sophisticated biological and biophysical data serve as an input to the computational models, and, in turn, these models answer questions that could not have been answered by experiments alone. This is partly due to experimental and . . . [Full Text of this Article]