The machine learning pipeline to unravel the functional effects of interactions between sodium channels.

Abstract

Recent reports obtained using patch clamp approach in Na+ channel (NaV) pairs have suggested cooperative gating of two NaVs, which has been linked to their homodimerization. Yet, the existing methods of the cooperative gating analysis are not effective in unraveling alterations in kinetics of individual NaVs result from their interactions in dimers. To address this issue, we developed a machine learning pipeline to remove capacitance drift and noise within the signal, and detect channel activity in order to infer Markov models parameters from single and multi-channel recordings.