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Modeling of Stochastic \(\mathrm{{Ca}}^{2+}\) Signals

  • Sten RüdigerEmail author
  • Jianwei Shuai
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI)

Abstract

It has been shown that IP\(_3\)R channels are distributed in clusters on the membrane of the endoplasmic reticulum, generating Ca\(^{2+}\) signals on multiple scales, from local puffs to global intra- and intercellular waves. Local Ca\(^{2+}\) puffs released from a cluster of IP\(_3\)R s are strongly stochastic. The most obvious source of noise for puffs is the small number of channels within a cluster. In this chapter we discuss the simulation of stochastic Ca\(^{2+}\) signals. Various simulation methods such as the Gillespie algorithm, a two-state Markovian chain, and gate-based and channel-based Langevin approaches have been introduced for use in the study of stochastic gating dynamics of IP\(_3\)R channels. Combining the stochastic channel dynamics with the deterministic simulation the Ca\(^{2+}\) diffusion process, the fluctuating Ca\(^{2+}\) signals, including puffs and both intra- and intercellular waves, can be investigated by hybrid models.

Keywords

Stochastic processes Calcium signaling Channel gating dynamics Inositol 1,4,5-trisphosphate receptor Calcium puffs Calcium sparks Calcium waves Intracellular waves Intercellular waves 

Notes

Acknowledgements

Shuai acknowledges support from the China National Funds for Distinguished Young Scholars under grant 11125419, the National Natural Science Foundation of China under grants 31370830 and 11675134, and the Fujian Province Funds for Leading Scientist in Universities. S.R. acknowledges support from the Deutsche Forschungsgemeinschaft (RU 1660 and IRTG 1740).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of PhysicsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Department of Physics and State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling NetworkXiamen UniversityXiamenChina

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