Analysis of single ion channel data incorporating time-interval omission and sampling.

Hidden Markov models are widely used to describe single channel currents from patch-clamp experiments. The inevitable anti-aliasing filter limits the time resolution of the measurements and therefore the standard hidden Markov model is not adequate anymore. The notion of time-interval omission has been introduced where brief events are not detected. The developed, exact solutions to this problem do not take into account that the measured intervals are limited by the sampling time. In this case the dead-time that specifies the minimal detectable interval length is not defined unambiguously. We show that a wrong choice of the dead-time leads to considerably biased estimates and present the appropriate equations to describe sampled data.

Modeling of single noninactivating Na+ channels: evidence for two open and several fast inactivated states.

Voltage-gated Na(+) channels play a fundamental role in the excitability of nerve and muscle cells. Defects in fast Na(+) channel inactivation can cause hereditary muscle diseases with hyper- or hypoexcitability of the sarcolemma. To explore the kinetics and gating mechanisms of noninactivating muscle Na(+) channels on a molecular level, we analyzed single channel currents from wild-type and five mutant Na(+) channels. The mutations were localized in different protein regions which have been previously shown to be important for fast inactivation (D3-D4-linker, D3/S4-S5, D4/S4-S5, D4/S6) and exhibited distinct grades of defective fast inactivation with varying levels of persistent Na(+) currents caused by late channel reopenings. Different gating schemes were fitted to the data using hidden Markov models with a correction for time interval omission and compared statistically. For all investigated channels including the wild-type, two open states were necessary to describe our data. Whereas one inactivated state was sufficient to fit the single channel behavior of wild-type channels, modeling the mutants with impaired fast inactivation revealed evidence for several inactivated states. We propose a single gating scheme with two open and three inactivated states to describe the behavior of all five examined mutants. This scheme provides a biological interpretation of the collected data, based on previous investigations in voltage-gated Na(+) and K(+) channels.

Estimating rate constants from single ion channel currents when the initial distribution is known.

Single ion channel currents can be analysed by hidden or aggregated Markov models. A classical result from Fredkin et al. (Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer, vol I, pp 269-289, 1985) states that the maximum number of identifiable parameters is bounded by 2n(o)n(c), where n(o) and n(c) denote the number of open and closed states, respectively. We show that this bound can be overcome when the probabilities of the initial distribution are known and the data consist of several sweeps.