Laboratory for Computational Biology and Biophysics, Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Anal Chem. 2012 May 1;84(9):3871-9. doi: 10.1021/ac2034369. Epub 2012 Apr 15.
Fluorescence correlation spectroscopy (FCS) is a powerful tool to infer the physical process of macromolecules including local concentration, binding, and transport from fluorescence intensity measurements. Interpretation of FCS data relies critically on objective multiple hypothesis testing of competing models for complex physical processes that are typically unknown a priori. Here, we propose an objective Bayesian inference procedure for testing multiple competing models to describe FCS data based on temporal autocorrelation functions. We illustrate its performance on simulated temporal autocorrelation functions for which the physical process, noise, and sampling properties can be controlled completely. The procedure enables the systematic and objective evaluation of an arbitrary number of competing, non-nested physical models for FCS data, appropriately penalizing model complexity according to the Principle of Parsimony to prefer simpler models as the signal-to-noise ratio decreases. In addition to eliminating overfitting of FCS data, the procedure dictates when the interpretation of model parameters are not justified by the signal-to-noise ratio of the underlying sampled data. The proposed approach is completely general in its applicability to transport, binding, or other physical processes, as well as spatially resolved FCS from image correlation spectroscopy, providing an important theoretical foundation for the automated application of FCS to the analysis of biological and other complex samples.
荧光相关光谱学（FCS）是一种强大的工具，可通过荧光强度测量推断大分子的物理过程，包括局部浓度、结合和传输。FCS 数据的解释严重依赖于对复杂物理过程的竞争模型进行客观的多假设检验，而这些复杂物理过程通常是事先未知的。在这里，我们提出了一种基于时间自相关函数的客观贝叶斯推断程序，用于测试多个竞争模型以描述 FCS 数据。我们通过模拟时间自相关函数来说明其性能，其中可以完全控制物理过程、噪声和采样特性。该程序能够系统地、客观地评估任意数量的竞争的、非嵌套的 FCS 数据物理模型，根据简约原则适当惩罚模型复杂度，以便随着信噪比的降低优先选择更简单的模型。除了消除 FCS 数据的过度拟合外，该程序还规定了当模型参数的解释不合理时，由基础采样数据的信噪比决定。该方法在应用于包括传输、结合或其他物理过程以及来自图像相关光谱学的空间分辨 FCS 方面具有完全的通用性，为自动将 FCS 应用于生物和其他复杂样品的分析提供了重要的理论基础。
