On the simple random-walk models of ion-channel gate dynamics reflecting long-term memory

Several approaches to ion-channel gating modelling have been proposed. Although many models describe the dwell-time distributions correctly, they are incapable of predicting and explaining the long-term correlations between the lengths of adjacent openings and closings of a channel. In this paper we propose two simple random-walk models of the gating dynamics of voltage and Ca2+-activated potassium channels which qualitatively reproduce the dwell-time distributions, and describe the experimentally observed long-term memory quite well. Biological interpretation of both models is presented. In particular, the origin of the correlations is associated with fluctuations of channel mass density. The long-term memory effect, as measured by Hurst R/S analysis of experimental single-channel patch-clamp recordings, is close to the behaviour predicted by our models. The flexibility of the models enables their use as templates for other types of ion channel

Optimal Estimation of Ion-Channel Kinetics from Macroscopic Currents

Markov modeling provides an effective approach for modeling ion channel kinetics. There are several search algorithms for global fitting of macroscopic or single-channel currents across different experimental conditions. Here we present a particle swarm optimization(PSO)-based approach which, when used in combination with golden section search (GSS), can fit macroscopic voltage responses with a high degree of accuracy (errors within 1%) and reasonable amount of calculation time (less than 10 hours for 20 free parameters) on a desktop computer. We also describe a method for initial value estimation of the model parameters, which appears to favor identification of global optimum and can further reduce the computational cost. The PSO-GSS algorithm is applicable for kinetic models of arbitrary topology and size and compatible with common stimulation protocols, which provides a convenient approach for establishing kinetic models at the macroscopic level

Fluorescence-Tracking of Activation Gating in Human ERG Channels Reveals Rapid S4 Movement and Slow Pore Opening

Background: hERG channels are physiologically important ion channels which mediate cardiac repolarization as a result of their unusual gating properties. These are very slow activation compared with other mammalian voltage-gated potassium channels, and extremely rapid inactivation. The mechanism of slow activation is not well understood and is investigated here using fluorescence as a direct measure of S4 movement and pore opening. Methods and Findings: Tetramethylrhodamine-5-maleimide (TMRM) fluorescence at E519 has been used to track S4 voltage sensor movement, and channel opening and closing in hERG channels. Endogenous cysteines (C445 and C449) in the S1–S2 linker bound TMRM, which caused a 10 mV hyperpolarization of the VK of activation to 227.562.0 mV, and showed voltage-dependent fluorescence signals. Substitution of S1–S2 linker cysteines with valines allowed unobstructed recording of S3–S4 linker E519C and L520C emission signals. Depolarization of E519C channels caused rapid initial fluorescence quenching, fit with a double Boltzmann relationship, F-VON, with VK,1 = 237.861.7 mV, and VK,2 = 43.567.9 mV. The first phase, VK,1, was,20 mV negative to the conductance-voltage relationship measured from ionic tail currents (G-VK = 218.361.2 mV), and relatively unchanged in a non-inactivating E519C:S620T mutant (V K = 234.461.5 mV), suggesting the fast initial fluorescence quenching tracked S4 voltage sensor movement. The second phase of rapid quenching was absent in the S620T mutant. The E519C fluorescence upon repolarizatio