PT  - JOURNAL ARTICLE
AU  - Kathryn E. Mangold
AU  - Wei Wang
AU  - Eric K. Johnson
AU  - Druv Bhagavan
AU  - Jonathan D. Moreno
AU  - Jeanne M. Nerbonne
AU  - Jonathan R. Silva
TI  - Identification of Structures for Ion Channel Kinetic Models
AID  - 10.1101/2021.04.06.438566
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.04.06.438566
4099  - http://biorxiv.org/content/early/2021/04/06/2021.04.06.438566.short
4100  - http://biorxiv.org/content/early/2021/04/06/2021.04.06.438566.full
AB  - 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 Statement Here, 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.