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Bayesian Inference Frameworks for Fluorescence Microscopy Data Analysis

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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…

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.
ContributorsWallgren, Ross (Author) / Presse, Steve (Thesis advisor) / Armbruster, Hans (Thesis advisor) / McCulloch, Robert (Committee member) / Arizona State University (Publisher)
Created2019
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Advancing Biophysics Research with Bayesian Methods: Novel Applications and Insights into Biological Systems' Behavior

Description
The Bayesian paradigm provides a flexible and versatile framework for modeling complex biological systems without assuming a fixed functional form or other constraints on the underlying data. This dissertation explores the use of Bayesian nonparametric methods for analyzing fluorescence microscopy data in biophysics, with a focus on enumerating diffraction-limited particles,…

The Bayesian paradigm provides a flexible and versatile framework for modeling complex biological systems without assuming a fixed functional form or other constraints on the underlying data. This dissertation explores the use of Bayesian nonparametric methods for analyzing fluorescence microscopy data in biophysics, with a focus on enumerating diffraction-limited particles, reconstructing potentials from trajectories corrupted by measurement noise, and inferring potential energy landscapes from fluorescence intensity experiments. This research demonstrates the power and potential of Bayesian methods for solving a variety of problems in fluorescence microscopy and biophysics more broadly.
ContributorsBryan IV, J Shepard (Author) / Presse, Steve (Thesis advisor) / Ozkan, Banu (Committee member) / Wadhwa, Navish (Committee member) / Shepherd, Doug (Committee member) / Arizona State University (Publisher)
Created2023
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Addition of Predatory Bacteria Decreases the Mortality of C. elegans

Description

Bdellovibrio bacteriovorus (BB) is a gram negative predatory bacteria that uses other gram negative bacteria to proliferate non-binarily. Due to the predatory nature of BB researchers have proposed to use it as a potential biocontrol agent against other gram negative bacteria. The in vivo effect of predatory bacteria on a…

Bdellovibrio bacteriovorus (BB) is a gram negative predatory bacteria that uses other gram negative bacteria to proliferate non-binarily. Due to the predatory nature of BB researchers have proposed to use it as a potential biocontrol agent against other gram negative bacteria. The in vivo effect of predatory bacteria on a living host lacks thorough investigation. This paper explores BB inside and outside of the C. elegans. BB acts internally by pre- infecting C. elegans with E. coli and then treating the worms with BB. After BB treatment worm survivavbility increased and morbidity decreased. Ex- ternally, BB modulated the environment around the nematode which reduced infection rates and increased nematode lifespan and survivability. Together, the internal and external results suggest BB has the capability to act as a living antibiotic acting topically and internally to reduce infection rates.
ContributorsStambolic, Milena (Author) / Presse, Steve (Thesis director) / Vlcek, Jessi (Committee member) / Barrett, The Honors College (Contributor) / Department of Physics (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05