Scientists are using data science tools to study molecular activity faster than traditional fluorescence correlation spectroscopy (FCS) allows. While FCS estimates dynamical quantities, it requires high signal-to-noise ratios and time traces in the microsecond range. The researchers are using Bayesian analysis to overcome the limitations of fluorescent correlative methods in deducing molecular properties such as diffusion coefficients from short and noisy time traces.
The researchers could analyze time traces that were too short to be analyzed by existing methods, including FCS, using Bayesian analysis (nonparametric) to directly analyze observed photon counts emitted by single molecules. The team’s new analysis method could allow single-molecule fluorescence confocal microscopy techniques to probe several orders of magnitude molecular processes faster. Furthermore, the new method may reduce the phototoxic effects on living samples that can occur when samples are exposed to light for extended periods.
Although single-molecule fluorescence techniques have transformed our understanding of the dynamics of many critical molecular processes, signals are inherently noisy, and experiments require long acquisition times. Old approaches limited scientists’ ability to investigate anything other than slow processes, leaving many intriguing biological questions involving faster chemical reactions out of reach. With Bayesian analysis, they can start asking questions about processes that will be resolved quickly.
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