Year: 2021
Type: review
Abstract: 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.
Source: Annual Review of Biophysics
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
Awards: The structural dynamics of translation elongation and its regulation, G1852020741, G1898198354, G1986115748, G2757594931, G2988778103, G365113682, G384430694, G5986892838, CAREER: The Role of Ribosome Conformational Dynamics on the Mechanism of Protein Synthesis, Label-Free, High-Time Resolution, Single-Molecule Studies of RNA Folding and Dynamics, G6671297155, G827198322, G857543222, G9000030609, G9000161191, G9000161536, G9000197387, G9000239804
OpenAlex ID: W3127412243