title: Using independent open-to-closed transitions to simplify aggregated Markov models of ion channel gating kinetics.
abstract: Deducing plausible reaction schemes from single-channel current traces is time-consuming and difficult. The goal is to find the simplest scheme that fits the data, but there are many ways to connect even a small number of states (>2 million schemes with four open and four closed states). Many schemes make identical predictions. An exhaustive search over model space does not address the many equivalent schemes that will result. We have found a canonical form that can express all reaction schemes for binary channels. This form has the minimal number of rate constants for any rank (number of independent open-closed transitions), unlike other canonical forms such as the well established "uncoupled" scheme. Because all of the interconductance transitions in the new form are independent, we refer to it as the manifest interconductance rank (MIR) form. In the case of four open and four closed states, there are four MIR form schemes, corresponding to ranks 1-4. For many models proposed in the literature for specific ion channels, the equivalent MIR form has dramatically fewer links than the uncoupled form. By using the MIR form we prove that all rank 1 topologies with a given number of open and closed states make identical predictions in steady state, thus narrowing the search space for simple models. Moreover, we prove that fitting to canonical form preserves detailed balance. We also propose an efficient hierarchical algorithm for searching for the simplest possible model consistent with a given data set.
authors.affiliation: Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
authors.forename: William J
authors.initials: WJ
authors.lastname: Bruno
authors.forename: Jin
authors.initials: J
authors.lastname: Yang
authors.forename: John E
authors.initials: JE
authors.lastname: Pearson
mesh_terms: D000465:Algorithms; D015640:Ion Channel Gating / Q000502:physiology*; D007700:Kinetics; D008390:Markov Chains; D015233:Models, Statistical; D015398:Signal Transduction