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Certain patterns of activity in the sympathetic nervous system can increase the propensity for cardiac arrhythmias; however, not much is known about what specific neural activity is problematic and how it induces pathology in the heart. Mathematical models have been built and used to simulate the effects of nervous system signaling on individual heart cells, but these models are highly complex and do not address specifically or quantitatively how signaling alters cardiac physiology. We use basic techniques to reduce the dimensionality of one such model, preserving the physiological detail but allowing us to examine the rate-determining steps for the system and the determinants of the complex behavior that results. We then apply dynamical systems techniques to the reduced model to analyze the parameter dependence, resulting in predictions for pharmacological intervention. Future work will extend this approach to the pacemaking region of the heart and the interactions between the sympathetic and parasympathetic nervous system.

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