A factor graph EM algorithm for inference of kinetic microstates from patch clamp measurements

TL;DR

This paper introduces a factor graph EM algorithm for accurately inferring hidden kinetic microstates from patch clamp data, demonstrating near-optimal performance and providing reliability scores for improved estimation.

Contribution

The paper develops a novel factor graph EM algorithm that combines parameter estimation and inference for kinetic microstates from patch clamp measurements, validated with simulations and real data.

Findings

Performance approaches theoretical maximum of MAP estimation

Reliability scores improve estimation accuracy

Effective on both simulated and real patch clamp data

Abstract

We derive a factor graph EM (FGEM) algorithm, a technique that permits combined parameter estimation and statistical inference, to determine hidden kinetic microstates from patch clamp measurements. Using the cystic fibrosis transmembrane conductance regulator (CFTR) and nicotinic acetylcholine receptor (nAChR) as examples, we perform {\em Monte Carlo} simulations to demonstrate the performance of the algorithm. We show that the performance, measured in terms of the probability of estimation error, approaches the theoretical performance limit of maximum {\em a posteriori} estimation. Moreover, the algorithm provides a reliability score for its estimates, and we demonstrate that the score can be used to further improve the performance of estimation. We use the algorithm to estimate hidden kinetic states in lab-obtained CFTR single channel patch clamp traces.