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Patch clamp data from the large conductance mechanosensitive channel (MscL) in E. coli was studied with the aim of developing a strategy for statistical analysis based on hidden Markov models (HMMs) and determining the number of conductance levels of the channel, together with mean current, mean dwell time and equilibrium probability of occupancy for each level. The models incorporated state-dependent white noise and moving average adjustment for filtering, with maximum likelihood parameter estimates obtained using an EM (expectation-maximisation) based iteration. Adjustment for filtering was included as it could be expected that the electronic filter used in recording would have a major effect on obviously brief intermediate conductance level sojourns. Preliminary data analysis revealed that the brevity of intermediate level sojourns caused difficulties in assignment of data points to levels as a result of over-estimation of noise variances. When reasonable constraints were placed on these variances using the better determined noise variances for the closed and fully open levels, idealisation anomalies were eliminated. Nevertheless, simulations suggested that mean sojourn times for the intermediate levels were still considerably over-estimated, and that recording bandwidth was a major limitation; improved results were obtained with higher bandwidth data (10 kHz sampled at 25 kHz). The simplest model consistent with these data had four open conductance levels, intermediate levels being approximately 20%, 51% and 74% of fully open. The mean lifetime at the fully open level was about 1 ms; estimates for the three intermediate levels were 54-92 micros, probably still over-estimates.

Patch clamp recordings from ion channels often show bursting behaviour, that is periods of repetitive activity, which are noticeably separated from each other by periods of inactivity. In this paper, bursting behaviour is considered for a general finite state space continuous-time Markov chain model of channel gating, allowing for one or more non-zero conductance levels. A unified semi-Markov framework, that encompasses both theoretical and empirical bursts, is described for analysing a broad range of properties of bursts, including the total charge transfer, the number of sojourns at distinct conductance levels and the number of openings in a burst, with the results presented when the channel is in equilibrium. When the gating mechanism is time reversible, it is shown that the distribution and autocorrelation function of each of the above properties are necessarily finite linear combinations of exponentially or geometrically decaying components with non-negative coefficients. Three methods for choosing a critical time for empirical bursts are investigated. The theory is illustrated by numerical examples from ryanodine, chloride and nicotinic acetylcholine receptor channels, demonstrating the power and flexibility of the methodology, and permitting comparison between the methods for choosing the critical time.

We consider a variety of Markov based models for systems of ion channels exhibiting dependence between channels. It is shown how many useful properties which may be calculated for an aggregated single-channel model, or a system of independent channels, can be extended to various types of interacting channel systems. Key structure and results from the theory of aggregated Markov processes are summarized in a convenient matrix form. These are then applied to the superposition of independent and dependent channels, including a patch of channels in a random environment, and a system of channels with spatial interactions. Calculations based on the resultant matrix expressions and intensity arguments can be implemented straightforwardly in a matrix-oriented package such as Matlab. The role of reversibility is also studied. A number of examples illustrate the strengths of the methods and enable numerical comparisons between the different types of systems.

The chloride selective channel from Torpedo electroplax, ClC-0, is the prototype of a large gene family of chloride channels that behave as functional dimers, with channel currents exhibiting two non-zero conductance levels. Each pore has the same conductance and is controlled by a subgate, and these have seemingly identical fast gating kinetics. However, in addition to the two subgates there is a single slower 'supergate' which simultaneously affects both channels. In the present paper, we consider a six state Markov model that is compatible with these observations and develop approximations as well as exact results for relevant properties of groupings of openings, known as bursts. Calculations with kinetic parameter values typical of ClC-0 suggest that even simple approximations can be quite accurate. Small deviations from the assumption of independence within the model lead to marked changes in certain predicted burst properties. This suggests that analysis of these properties may be helpful in assessing independence/non-independence of gating in this type of channel. Based on simulations of models of both independent and non-independent gating, tests using binomial distributions can lead to false conclusions in each situation. This is made more problematic by the difficulty of selecting an appropriate critical time in defining a burst empirically.

Allosteric mechanisms have been suggested for an increasing number of ion channel drug interactions, but often these ideas are not examined quantitatively through use of Markov models that would allow statistical estimation of proposed coupling effects. In this paper we illustrate, using properties relevant to the neuronal nicotinic acetylcholine receptor, how these models can be used to provide insight into the behavior of cooperative systems. Such models would then provide the basis for inferential studies with experimental data aimed at quantifying the magnitude of drug-induced changes on particular channel parameters. It is shown that even small changes in agonist binding affinity or channel gating are sufficient to produce biphasic modulatory drug effects in an allosteric model of nicotinic receptor activity., (Copyright 2000 Academic Press.)

Concentration-dependent biphasic effects of drugs on ion channel activity have been reported in a variety of preparations, usually with stimulatory effects seen at low concentrations followed by increasingly dominant inhibition at higher levels. Such behaviour is often interpreted as evidence for the existence of separate modulatory drug binding sites. We demonstrate in this paper that it is possible for biphasic effects to be produced in an allosteric model of a ligand-activated ion channel, where diffusion-limited binding of the modulatory drug is restricted to either a stimulatory or an inhibitory site (but not both) because of steric overlap. The possibility of such an interaction mechanism should be kept in mind when interpreting experimental data if stoichiometric evidence from complementary techniques suggests that only one drug molecule is bound per receptor/ion channel complex., (Copyright 1998 Elsevier Science B.V. All rights reserved.)

Quantitative analysis of patch clamp data is widely based on stochastic models of single-channel kinetics. Membrane patches often contain more than one active channel of a given type, and it is usually assumed that these behave independently in order to interpret the record and infer individual channel properties. However, recent studies suggest there are significant channel interactions in some systems. We examine a model of dependence in a system of two identical channels, each modeled by a continuous-time Markov chain in which specified transition rates are dependent on the conductance state of the other channel, changing instantaneously when the other channel opens or closes. Each channel then has, e.g., a closed time density that is conditional on the other channel being open or closed, these being identical under independence. We relate the two densities by a convolution function that embodies information about, and serves to quantify, dependence in the closed class. Distributions of observable (superposition) sojourn times are given in terms of these conditional densities. The behavior of two channel systems based on two- and three-state Markov models is examined by simulation. Optimized fitting of simulated data using reasonable parameters values and sample size indicates that both positive and negative cooperativity can be distinguished from independence.

Models of ion channel kinetics subserve inferential methods applied to patch clamp data. For Markov models the density function of a sojourn time in a class of states is a mixture of exponentials. Determination of kinetic parameters from density functions may be complicated by non-uniqueness of solutions. This non-identifiability is investigated analytically for a class of two states, assuming detailed balance; relations between model properties, observable density parameters, and non-uniqueness are presented. The results are further developed in terms of similarity transform methods. Additional information provided by joint distributions is discussed. An example is given where identifiability of a model can be demonstrated explicitly. Attention is drawn to instances where the number of components in a density function may be misleading when used to infer the number of underlying states.

We present a general theoretical framework, incorporating both aggregation of states into classes and time interval omission, for stochastic modeling of the dynamic aspects of single channel behavior. Our semi-Markov models subsume the standard continuous-time Markov models, diffusion models and fractal models. In particular our models allow for quite general distributions of state sojourn times and arbitrary correlations between successive sojourn times. Another key feature is the invariance of our framework with respect to time interval omission: that is, properties of the aggregated process incorporating time interval omission can be derived directly from corresponding properties of the process without it. Even in the special case when the underlying process is Markov, this leads to considerable clarification of the effects of time interval omission. Among the properties considered are equilibrium behavior, sojourn time distributions and their moments, and auto-correlation and cross-correlation functions. The theory is motivated by ion channel mechanisms drawn from the literature, and illustrated by numerical examples based on these.

This paper considers the distribution of a sojourn time in a class of states of a stochastic process having finite discrete state space where sojourn times in any individual state are independent and identically distributed, and transitions between states follow a Markov chain. The state space and possible transitions of the process are represented by a graph. Class sojourn time distributions are derived by modifying this graph using 'composition' of states, defining a new Markov chain on the modified graph, and expressing the sojourn time in a composition state as a random sum. Appropriate compositions are chosen according to the possible "cores" of sojourns in the particular class, where a core describes the structure of a sojourn in terms of a single state or a chain in the original graph. Graph methods provide an algorithmic basis for the derivation, which can be simplified by using symmetry results. Models of ion-channel kinetics are used throughout for illustration; class sojourn time distributions are important in such models because individual states are often indistinguishable experimentally. Markov processes are the special case where sojourn times in individual states are exponentially distributed. In this case kinetic parameter estimation based on the observed class sojourn time distribution is briefly discussed; explicit estimating equations applicable to sequential models of nicotinic receptor kinetics are given.

Membrane patches usually contain several ion channels of a given type. However, most of the stochastic modelling on which data analysis (in particular, estimation of kinetic constants) is currently based, relates to a single channel rather than to multiple channels. Attempts to circumvent this problem experimentally by recording under conditions where channel activity is low are restrictive and can introduce bias; moreover, possibly important information on how multichannel systems behave will be missed. We have extended existing theory to multichannel systems by applying results from point process theory to derive some distributional properties of the various types of sojourn time that occur when a given number of channels are open in a system containing a specified number of independent channels in equilibrium. Separate development of properties of a single channel and the superposition of several such independent channels simplifies the presentation of known results and extensions. To illustrate the general theory, particular attention is given to the types of sojourn time that occur in a two channel system; detailed expressions are presented for a selection of models, both Markov and non-Markov.

Though stochastic models are widely used to describe single ion channel behaviour, statistical inference based on them has received little consideration. This paper describes techniques of statistical inference, in particular likelihood methods, suitable for Markov models incorporating limited time resolution by means of a discrete detection limit. To simplify the analysis, attention is restricted to two-state models, although the methods have more general applicability. Non-uniqueness of the mean open-time and mean closed-time estimators obtained by moment methods based on single exponential approximations to the apparent open-time and apparent closed-time distributions has been reported. The present study clarifies and extends this previous work by proving that, for such approximations, the likelihood equations as well as the moment equations (usually) have multiple solutions. Such non-uniqueness corresponds to non-identifiability of the statistical model for the apparent quantities. By contrast, higher-order approximations yield theoretically identifiable models. Likelihood-based estimation procedures are developed for both single exponential and bi-exponential approximations. The methods and results are illustrated by numerical examples based on literature and simulated data, with consideration given to empirical distributions and model control, likelihood plots, and point estimation and confidence regions.

Stochastic models of ion channels have been based largely on Markov theory where individual states and transition rates must be specified, and sojourn-time densities for each state are constrained to be exponential. This study presents an approach based on random-sum methods and alternating-renewal theory, allowing individual states to be grouped into classes provided the successive sojourn times in a given class are independent and identically distributed. Under these conditions Markov models form a special case. The utility of the approach is illustrated by considering the effects of limited time resolution (modelled by using a discrete detection limit, xi) on the properties of observable events, with emphasis on the observed open-time (xi-open-time). The cumulants and Laplace transform for a xi-open-time are derived for a range of Markov and non-Markov models; several useful approximations to the xi-open-time density function are presented. Numerical studies show that the effects of limited time resolution can be extreme, and also highlight the relative importance of the various model parameters. The theory could form a basis for future inferential studies in which parameter estimation takes account of limited time resolution in single channel records. Appendixes include relevant results concerning random sums and a discussion of the role of exponential distributions in Markov models.

The limited responsiveness of single-channel recording systems results in some brief events not being detected, and if this is ignored parameter estimation from the observed data will be biased. Statistical methods of correcting for this limited time resolution in a two-state Markov model have been proposed by Neher (1983. J. Physiol. (Lond.). 339:663-678) and by Colquhoun and Sigworth (1983. Single Channel Recording. 191-263). However, a numerical study by Blatz and Magleby (1986. Biophys. J. 49:967-980) indicated differences of 3-40% in the corrected values given by the two techniques. Here we explain why Neher's method produces biased results and the Colquhoun and Sigworth approach, which is no more difficult, provides reasonably accurate estimates.