Identification of structures for ion channel kinetic models

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.

Identification of Structures for Ion Channel Kinetic Models

Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function. Past studies have examined 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 and human left ventricular fast transient outward potassium currents. In addition to optional biophysically inspired restrictions on the number of connections from a state and elimination of long-range connections, this study further suggests successful models have more than minimum number of connections for set number of states. When working with topologies with more than the minimum number of connections, the topologies with three and four connections to the open state tend to serve well as Markov models of ion channel dynamics.Significance StatementHere, we present a computational routine to thoroughly search for Markov model topologies for simulating whole-cell currents given an experimental dataset. We test this method on two distinct types of voltage-gated ion channels that function in the generation of cardiac action potentials. Particularly successful models have more than one connection between an open state and the rest of the model, and large models may benefit from having even more connections between the open state and the rest of the other states.

Mechanisms of atrial-selective block of Na+ channels by ranolazine: II. Insights from a mathematical model

Block of Na+ channel conductance by ranolazine displays marked atrial selectivity that is an order of magnitude higher that of other class I antiarrhythmic drugs. Here, we present a Markovian model of the Na+ channel gating, which includes activation-inactivation coupling, aimed at elucidating the mechanisms underlying this potent atrial selectivity of ranolazine. The model incorporates experimentally observed differences between atrial and ventricular Na+ channel gating, including a more negative position of the steady-state inactivation curve in atrial versus ventricular cells. The model assumes that ranolazine requires a hydrophilic access pathway to the channel binding site, which is modulated by both activation and inactivation gates of the channel. Kinetic rate constants were obtained using guarded receptor analysis of the use-dependent block of the fast Na+ current ( INa). The model successfully reproduces all experimentally observed phenomena, including the shift of channel availability, the sensitivity of block to holding or diastolic potential, and the preferential block of slow versus fast INa. Using atrial and ventricular action potential-shaped voltage pulses, the model confirms significantly greater use-dependent block of peak INa in atrial versus ventricular cells. The model highlights the importance of action potential prolongation and of a steeper voltage dependence of the time constant of unbinding of ranolazine from the atrial Na+ channel in the development of use-dependent INa block. Our model predictions indicate that differences in channel gating properties as well as action potential morphology between atrial and ventricular cells contribute equally to the atrial selectivity of ranolazine. The model indicates that the steep voltage dependence of ranolazine interaction with the Na+ channel at negative potentials underlies the mechanism of the predominant block of INa in atrial cells by ranolazine.