2023-05-30T14:13:46Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1571212021-08-27T02:47:01Zoai_pmh:alloai_pmh:repo_items157121
https://hdl.handle.net/2286/R.I.53545
http://rightsstatements.org/vocab/InC/1.0/
2019
iv, 64 pages : illustrations (some color)
Masters Thesis
Academic theses
Text
eng
Wallgren, Ross
Presse, Steve
Armbruster, Hans
McCulloch, Robert
Arizona State University
Partial requirement for: M.A., Arizona State University, 2019
Includes bibliographical references (pages 32-36)
Field of study: Applied mathematics
In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscopy data, benchmarking the method on synthetic data. These three frameworks are self-contained, and can be used concurrently so that the fluorescence profile and emitter locations are both considered unknown and, under some conditions, learned simultaneously. The framework I present is flexible and may be adapted to accommodate the inference of other parameters, such as emission photophysical kinetics and the trajectories of moving molecules. My TIRF-specific implementation may find use in the study of structures on cell membranes, or in studying local sample properties that affect fluorescent molecule photon emission rates.
Mathematics
Statistics
Biophysics
Bayesian
beta-bernoulli process
gaussian process
Markov Chain Monte Carlo
Microscopy
superresolution
Fluorescence microscopy--Mathematical models.
Fluorescence Microscopy
Bayesian statistical decision theory--Scientific applications.
Bayesian statistical decision theory
Bayesian Inference Frameworks for Fluorescence Microscopy Data Analysis