Computational Modeling of Cyclic Nucleotide Signaling Mechanisms in Cardiac Myocytes

Chapter
Part of the Cardiac and Vascular Biology book series (Abbreviated title: Card. vasc. biol.)

Abstract

The balanced signaling between the two cyclic nucleotides (cNs), cAMP and cGMP, in the cN signaling system plays a critical role in regulating cardiac contractility. Many therapeutic agents have been developed to selectively inhibit or stimulate proteins in the cN signaling system in the attempt to manage and treat heart diseases. Nonetheless, it has been challenging to obtain a comprehensive, system-level understanding of the signal transduction mechanisms, in part because of the participation of multiple phosphodiesterases (PDEs) in the common task of cN degradation, the complex interactions between the signaling proteins, and the large number of cN regulated targets in the tightly coupled excitation-contraction (EC) coupling process. Multi-scale, biophysically detailed, and experimentally validated computational models are well suited to dissect the underlying mechanisms in these nonlinear and intertwined reaction networks. By precisely defining and quantifying biochemical reactions involved, data-driven and integrative modeling bridge causal gaps across spatiotemporal scale, from the characteristics of individual molecular components to the collective responses of the entire signaling network. Through predictive modeling and in-depth analysis, these computational models are powerful in providing insights into cellular mechanisms, formulating novel hypothesis, and proposing possible future experiments. This review focuses on the development of mechanistic models, the close interplay between modeling and experimentation, and the identification of opportunities for future modeling research in the cardiac myocyte cN signaling system.

Keywords

Cyclic nucleotides Phosphodiesterases Signaling network Electrophysiology Compartmentation Heart failure Cardiac myocytes Computational modeling 

Notes

Acknowledgments

This work was supported by Natural Sciences and Engineering Research Council (NSERC) of Canada scholarships, CGS M-377616-2009 and PGSD3-405041-2011, awarded to C.Y.Z.

Compliance with Ethical Standards

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringInstitute for Computational Medicine, The Johns Hopkins University School of Medicine and Whiting School of EngineeringBaltimoreUSA
  2. 2.Acute Care Solutions, Philips Research North AmericaCambridgeUSA

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