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Comparison of Models for IP3 Receptor Kinetics Using Stochastic Simulations

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  • Katri Hituri
  • Marja-Leena Linne

Abstract

Inositol 1,4,5-trisphosphate receptor (IP3R) is a ubiquitous intracellular calcium (Ca2+) channel which has a major role in controlling Ca2+ levels in neurons. A variety of computational models have been developed to describe the kinetic function of IP3R under different conditions. In the field of computational neuroscience, it is of great interest to apply the existing models of IP3R when modeling local Ca2+ transients in dendrites or overall Ca2+ dynamics in large neuronal models. The goal of this study was to evaluate existing IP3R models, based on electrophysiological data. This was done in order to be able to suggest suitable models for neuronal modeling. Altogether four models (Othmer and Tang, 1993; Dawson et al., 2003; Fraiman and Dawson, 2004; Doi et al., 2005) were selected for a more detailed comparison. The selection was based on the computational efficiency of the models and the type of experimental data that was used in developing the model. The kinetics of all four models were simulated by stochastic means, using the simulation software STEPS, which implements the Gillespie stochastic simulation algorithm. The results show major differences in the statistical properties of model functionality. Of the four compared models, the one by Fraiman and Dawson (2004) proved most satisfactory in producing the specific features of experimental findings reported in literature. To our knowledge, the present study is the first detailed evaluation of IP3R models using stochastic simulation methods, thus providing an important setting for constructing a new, realistic model of IP3R channel kinetics for compartmental modeling of neuronal functions. We conclude that the kinetics of IP3R with different concentrations of Ca2+ and IP3 should be more carefully addressed when new models for IP3R are developed.

Suggested Citation

  • Katri Hituri & Marja-Leena Linne, 2013. "Comparison of Models for IP3 Receptor Kinetics Using Stochastic Simulations," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-13, April.
  • Handle: RePEc:plo:pone00:0059618
    DOI: 10.1371/journal.pone.0059618
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    References listed on IDEAS

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    1. Robert C Cannon & Cian O'Donnell & Matthew F Nolan, 2010. "Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-18, August.
    2. Antti Saarinen & Marja-Leena Linne & Olli Yli-Harja, 2008. "Stochastic Differential Equation Model for Cerebellar Granule Cell Excitability," PLOS Computational Biology, Public Library of Science, vol. 4(2), pages 1-11, February.
    3. Manuel Barrio & Kevin Burrage & André Leier & Tianhai Tian, 2006. "Oscillatory Regulation of Hes1: Discrete Stochastic Delay Modelling and Simulation," PLOS Computational Biology, Public Library of Science, vol. 2(9), pages 1-14, September.
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