Graphical models for inferring single molecule dynamics

TL;DR

This paper presents a Bayesian graphical modeling approach using VBEM for analyzing single-molecule time series data, exemplified by smFRET, enabling improved model inference and selection.

Contribution

It introduces a variational Bayesian framework for graphical models applied to single-molecule data, enhancing inference and model selection over traditional methods.

Findings

VBEM provides model evidence and posterior distributions.

Model selection via maximum evidence is effective.

Graphical models improve inference of molecular dynamics.

Abstract

Background: The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well. Results: The VBEM algorithm returns the model's evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model's parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem. Conclusions: The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.