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The heart is characterized by a complex electromechanical activity essential for the sustenance of body function. Cardiac disease is the leading cause of morbidity and mortality in the industrialized world,1 imposing a major burden on health care systems. Current therapies rely, to a large extent, on implantable devices, administration of drugs, or the ablation of tissue. Although these therapies may improve a patient's condition significantly, they are palliative rather than curative, and undesired adverse effects of varying degrees of severity are quite common.2-5 In the quest of devising novel, safer, and more effective therapies to reduce medical costs and treatment duration, developing a comprehensive understanding of cardiac structure and function in health and disease is the strategy most likely to succeed and, thus, is a central focus of basic and clinical heart research. Traditionally, experimental work in conjunction with clinical studies was the dominant, if not exclusive, approach for inquiries into physiological function. Today, in the postgenomic era, a wider portfolio of techniques is employed, with computational modeling being another accepted approach, either as a complement to experimental work or as a stand-alone tool for exploratory testing of hypotheses. The need for computational modeling is mainly driven by the difficulty in dealing with the vast amount of available data, obtained from various subsystems of the heart, at different hierarchical levels of organization from different species, and with the complexity involved in the dynamics of interactions between subsystems within and across levels of organization. Computational modeling plays a pivotal, if not indispensable, role in harnessing these data for further advancing our mechanistic understanding of cardiac function.

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Motivation for Using in Silico Approaches

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Over the past decade, impressive advances in experimental technology have generated a wealth of information across all levels of biological organization for various species, including humans, ranging from the genetic scale of the cardiac system up to the entire organ. However, attempts to translate these large quantities of experimental data into safer and more effective therapies to the benefit of patients have largely proved to be elusive. In no small part, this results from the complexity of biological systems under study. They invariably comprise multiple subsystems, interacting with each other within and across levels of organization. Although biological systems can be dissected into many subcomponents, there is now a clear recognition that the interaction within and across levels of organization may produce emergent properties that simply cannot be predicted from "reductionist" analysis. Because these emerging properties are often not intuitive and not predictable from analyzing the subsystems in isolation, detailed understanding of individual subsystems may provide little mechanistic insight into functions at the organ level.6 Moreover, due to the highly complex nonlinear relationship between a rapidly increasing amount of disparate experimental data on an increasingly larger number of biological subsystems involved, any attempts to gain new mechanistic insights at the level of a system by deriving a qualitative understanding of all the simultaneous interactions within and ...

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