Thesis and Dissertation Defenses
Bayesian Analysis of Single Molecule Fluorescence Microscopy Data
Presented by Mohamadreza Fazel
Single molecule localization microscopy (SMLM) has made significant contributions to shedding light on numerous biological problems by breaking the Abbe diffraction barrier of resolution in light microscopy. In these approaches, the target is labeled with fluorescent dyes which can be localized from image frames enerated
by sparse activation of the dyes at each time. The resulting list of localizations is then used to reconstruct an image with sub-diffraction resolution.
In this dissertation, Reversible Jump Markov Chain Monte Carlo was employed to implement Bayesian analysis and post-processing of single molecule fluorescence microscopy data. Bayesian multiple-emitter
fitting (BAMF) was developed to localize emitters in dense and noisy regions of data. This technique is particularly advantageous in fitting emitters in close spatial proximity and recognizing heterogeneous background noise.
In the list of localizations produced in a SMLM experiment, each emitter is represented by multiple localizations generated from several blinking events over the course of data acquisition. Bayesian grouping of localizations (BaGoL) produces emitter locations with
enhanced precisions by identifying and combining the subset of localizations from each emitter. BaGoL advances the state-of-the-art in quantifying the geometrical distribution of particles in biological samples by producing emitter locations with sub-nanometer
precision.
1:00 pm, Friday, May 8, 2020
Zoom,
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