Referenced in: none

Automatically associated channels: Cav2.1 , Slo1

Title: Modeling a Ca(2+) channel/BKCa channel complex at the single-complex level.

Authors: Daniel H Cox

Journal, date & volume: Biophys. J., 2014 Dec 16 , 107, 2797-814

PubMed link: http://www.ncbi.nlm.nih.gov/pubmed/25517147

Abstract
BKCa-channel activity often affects the firing properties of neurons, the shapes of neuronal action potentials (APs), and in some cases the extent of neurotransmitter release. It has become clear that BKCa channels often form complexes with voltage-gated Ca(2+) channels (CaV channels) such that when a CaV channel is activated, the ensuing influx of Ca(2+) activates its closely associated BKCa channel. Thus, in modeling the electrical properties of neurons, it would be useful to have quantitative models of CaV/BKCa complexes. Furthermore, in a population of CaV/BKCa complexes, all BKCa channels are not exposed to the same Ca(2+) concentration at the same time. Thus, stochastic rather than deterministic models are required. To date, however, no such models have been described. Here, however, I present a stochastic model of a CaV2.1/BKCa(α-only) complex, as might be found in a central nerve terminal. The CaV2.1/BKCa model is based on kinetic modeling of its two component channels at physiological temperature. Surprisingly, The CaV2.1/BKCa model predicts that although the CaV channel will open nearly every time during a typical cortical AP, its associated BKCa channel is expected to open in only 30% of trials, and this percentage is very sensitive to the duration of the AP, the distance between the two channels in the complex, and the presence of fast internal Ca(2+) buffers. Also, the model predicts that the kinetics of the BKCa currents of a population of CaV2.1/BKCa complexes will not be limited by the kinetics of the CaV2.1 channel, and during a train of APs, the current response of the complex is expected to faithfully follow even very rapid trains. Aside from providing insight into how these complexes are likely to behave in vivo, the models presented here could also be of use more generally as components of higher-level models of neural function.