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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 1613290, 10 pages
Research Article

The Application of Dynamic Models to the Exploration of -AR Overactivation as a Cause of Heart Failure

1College of Mathematics, Taiyuan University of Technology, Taiyuan, Shanxi, China
2Department of Scientific Computing, Florida State University, Tallahassee, FL, USA
3Department of Mathematics, North University of China, Taiyuan, Shanxi, China
4Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
5Beijing Key Laboratory of Metabolic Disorders Related Cardiovascular Diseases, Capital Medical University, Beijing, China

Correspondence should be addressed to Xiaoyun Wang; moc.621@8070gnawyx, Xiaoqiang Wang; ude.usf@3gnaww, and Huirong Liu; moc.621@0002rhuil

Received 7 March 2018; Revised 17 May 2018; Accepted 28 May 2018; Published 30 July 2018

Academic Editor: Jesús Poza

Copyright © 2018 Xiaoyun Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


High titer of -adrenoreceptor autoantibodies (-AA) has been reported to appear in heart failure patients. It induces sustained -adrenergic receptor (-AR) activation which leads to heart failure (HF), but the mechanism is as yet unclear. In order to investigate the mechanisms causing -AR non-desensitization, we studied the beating frequency of the neonatal rat cardiomyocytes (NRCMs) under different conditions (an injection of isoprenaline (ISO) for one group and -AA for the other) and established three dynamic models in order to best describe the true relationships shown in medical experiments; one model used a control group of healthy rats; then in HF rats one focused on conformation changes in -AR; the other examined interaction between -AR and -adrenergic receptors (-AR). Comparing the experimental data and corresponding Akaike information criterion (AIC) values, we concluded that the interaction model was the most likely mechanism. We used mathematical methods to explore the mechanism for the development of heart failure and to find potential targets for prevention and treatment. The aim of the paper was to provide a strong theoretical basis for the clinical development of personalized treatment programs. We also carried out sensitivity analysis of the initial concentration -AA and found that they had a noticeable effect on the fitting results.