Proceedings of the Royal Society of London. Series B, Biological sciences · 1989 · 159 citations · 61 references
EngineeringIon-channel KineticsEntropyIon-channel GatingSimilarity TransformationHidden Markov ModelGibbs MeasureMarkov KernelInteracting Particle SystemProbability TheoryModeling And SimulationElectrophysiologyGenerator MatricesBiophysicsTheoretical Modeling
One cannot always distinguish different Markov models of ion-channel kinetics solely on the basis of steady-state kinetic data. If two generator (or transition) matrices are related by a similarity transformation that does not combine states with different conductances, then the models described by these generator matrices have the same observable steady-state statistics. This result suggests a procedure for expressing the model in a unique form, and sometimes reducing the number of parameters in a model. I apply the similarity transformation procedure to a number of simple models. When a model specifies the dependence of the rates of transition on an experimentally variable parameter such as the concentration of a ligand or the membrane potential, the class of equivalent models may be further restricted, but a model is not always uniquely determined even under these conditions. Voltage-step experiments produce non-stationary data that can also be used to distinguish models.
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