Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments

Colin D. Kinz-Thompson, Korak Kumar Ray, Ruben L. Gonzalez

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)191-208
Number of pages18
JournalAnnual Review of Biophysics
StatePublished - May 6 2021

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Structural Biology
  • Bioengineering
  • Biochemistry
  • Cell Biology


  • cryo-EM
  • error propagation
  • kinetics
  • model selection
  • probability theory
  • scientific method


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