Abstract
Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function. Past studies have examined limited sets of various topologies for Markov models of channel dynamics. We present a systematic method for identification of all possible Markov model topologies using experimental data for two types of native voltage-gated ion channel currents: mouse atrial sodium currents and human left ventricular fast transient outward potassium currents. Successful models identified with this approach have certain characteristics in common, suggesting that aspects of the model topology are determined by the experimental data. Incorporating these channel models into cell and tissue simulations to assess model performance within protocols that were not used for training provided validation and further narrowing of the number of acceptable models. The success of this approach suggests a channel model creation pipeline may be feasible where the structure of the model is not specified a priori.
Highlights
Discrete state Markov, or state-dependent, models have been used extensively to probe the role of ion channel dynamics in generating the excitability of neurons [1], cardiac myocytes [2], and pancreatic beta cells [3,4]
Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function
We present a computational routine designed to thoroughly search for Markov model topologies for simulating whole-cell currents. We tested this method on two distinct types of voltage-gated cardiac ion channels and found the number of states and connectivity required to recapitulate experimentally observed kinetics
Summary
Discrete state Markov, or state-dependent, models have been used extensively to probe the role of ion channel dynamics in generating the excitability of neurons [1], cardiac myocytes [2], and pancreatic beta cells [3,4]. Markov models recapitulate channel dynamics by discretizing behavior into a series of states, with transitions between states governed by rate constants that often vary as a function of membrane potential [5]. These Markov models are inserted into cellular models to simulate action potential waveforms and frequency-dependent properties [3,6,7,8]. For the voltage-gated cardiac Na+ channel, for example, current Markov models reflect multiple stages of channel activation, deactivation, and inactivation from both closed and open states [14,15]
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