Laboratory for Computational Biology and Biophysics, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Anal Chem. 2012 May 1;84(9):3880-8. doi: 10.1021/ac2034375. Epub 2012 Apr 15.
Fluorescence correlation spectroscopy (FCS) is a powerful approach to characterizing the binding and transport dynamics of macromolecules. The unbiased interpretation of FCS data relies on the evaluation of multiple competing hypotheses to describe an underlying physical process under study, which is typically unknown a priori. Bayesian inference provides a convenient framework for this evaluation based on the temporal autocorrelation function (TACF), as previously shown theoretically using model TACF curves (He, J., Guo, S., and Bathe, M. Anal. Chem. 2012, 84). Here, we apply this procedure to simulated and experimentally measured photon-count traces analyzed using a multitau correlator, which results in complex noise properties in TACF curves that cannot be modeled easily. As a critical component of our technique, we develop two means of estimating the noise in TACF curves based either on multiple independent TACF curves themselves or a single raw underlying intensity trace, including a general procedure to ensure that independent, uncorrelated samples are used in the latter approach. Using these noise definitions, we demonstrate that the Bayesian approach selects the simplest hypothesis that describes the FCS data based on sampling and signal limitations, naturally avoiding overfitting. Further, we show that model probabilities computed using the Bayesian approach provide a reliability test for the downstream interpretation of model parameter values estimated from FCS data. Our procedure is generally applicable to FCS and image correlation spectroscopy and therefore provides an important advance in the application of these methods to the quantitative biophysical investigation of complex analytical and biological systems.
荧光相关光谱学（FCS）是一种用于描述大分子结合和输运动力学的强大方法。无偏的 FCS 数据分析依赖于对多个竞争性假设的评估，以描述研究中未知的基本物理过程。贝叶斯推断为基于时间自相关函数（TACF）的评估提供了一个方便的框架，这在之前的理论中已经使用模型 TACF 曲线进行了展示（He，J.，Guo，S.和 Bathe，M. Anal. Chem. 2012, 84）。在这里，我们将此过程应用于使用多时间相关器分析的模拟和实验测量的光子计数轨迹，这导致 TACF 曲线中出现复杂的噪声特性，难以进行简单建模。作为我们技术的关键组成部分，我们开发了两种方法来估计 TACF 曲线中的噪声，一种方法是基于多个独立的 TACF 曲线本身，另一种方法是基于单个原始强度轨迹，包括一种通用程序来确保在后一种方法中使用独立、不相关的样本。使用这些噪声定义，我们证明了贝叶斯方法根据采样和信号限制选择最简单的假设来描述 FCS 数据，自然避免了过度拟合。此外，我们还表明，使用贝叶斯方法计算的模型概率为从 FCS 数据估计的模型参数值的下游解释提供了可靠性测试。我们的程序通常适用于 FCS 和图像相关光谱学，因此为这些方法在复杂分析和生物系统的定量生物物理研究中的应用提供了重要进展。
