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Fluorescence Correlation Spectroscopy (FCS) is a powerful approach to measuring biomolecular dynamics in living and reconstituted systems. Inferring the physical process underlying measured fluorescence fluctuations, including species concentrations, mode of transport, and binding reactions, requires the objective, side-by-side evaluation of multiple competing hypotheses based on FCS data. Bayesian inference provides a practical framework for this multiple hypothesis testing to be performed in an unbiased, automated manner accounting for sampling noise and model complexity. This approach is particularly useful for the analysis of novel biological and chemical systems for which the underlying physical process is unknown a priori, and imaging FCS data which typically contains hundreds to thousands of correlation functions in a single measurement. Our proposed approach operates directly either on raw fluorescence intensity traces or temporal autocorrelation functions. A Bayesian procedure for multiple hypothesis testing of FCS data is available here for download as a set of MATLAB resource files. Please visit MSD-Bayes for a related approach to analyzing single-particle motion based on single-particle tracking data.

 

The FCS-Bayes package has been downloaded 165 times