Open AccessBayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments
Author(s) -
Colin D. Kinz-Thompson,
Korak Kumar Ray,
Ruben L. Gonzalez
Publication year - 2021
Publication title -
annual review of biophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.144
H-Index - 159
eISSN - 1936-1238
pISSN - 1936-122X
DOI - 10.1146/annurev-biophys-082120-103921
Subject(s) - overfitting , bayesian probability , inference , computer science , bayesian inference , set (abstract data type) , bayesian experimental design , bayesian statistics , experimental data , machine learning , artificial intelligence , data set , data mining , algorithm , mathematics , statistics , artificial neural network , programming language
Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.