Kinz-Thompson Colin D, Ray Korak Kumar, Gonzalez Ruben L
Department of Chemistry, Columbia University, New York, New York 10027, USA; email:
Department of Chemistry, Rutgers University-Newark, Newark, New Jersey 07102, USA.
Annu Rev Biophys. 2021 May 6;50:191-208. doi: 10.1146/annurev-biophys-082120-103921. Epub 2021 Feb 3.
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.
在单分子分辨率下进行的生物物理实验能够为生物系统的结构细节和动态行为提供非凡的见解。然而，要明确地从相应的实验数据中提取这些信息，就需要应用生物物理模型。在这篇综述中，我们讨论如何运用概率论将这些模型应用于单分子数据。当前许多单分子数据分析方法有时在不知不觉中应用了概率论的部分内容，因此错失了这个自洽框架所提供的全部益处。概率论的全面应用涉及一个称为贝叶斯推理的过程，该过程充分考虑了单分子实验中固有的不确定性。此外，使用贝叶斯推理提供了一种科学严谨的方法，可将来自多个实验的信息整合到单一分析中，并找到适合实验的最佳生物物理模型，同时避免数据过度拟合的风险。这些益处使得贝叶斯方法成为分析任何类型单分子实验的理想选择。
