Uncovering Hidden Dynamics in Living Systems Using Bayesian Statistics and Single-Molecule Microscopy
Karslake, Josh
2019
Abstract
Fluorescence microscopy is a powerful technique for understanding the organization, structure and dynamics of cells. Single-molecule imaging techniques extend our ability to probe cellular systems down to a range of only a few tens of nanometers. Observing the motion of single molecules inside living cells and tracking their behavior can give insight into the native biochemical and biophysical environment of the molecule. If certain conditions, such as the cell being in equilibrium, are met, we can relate the motion observed to the functional role of the molecule. However, biological systems are complex and single-molecule data can be noisy, so care must be taken when analyzing single-particle tracking data sets such that supervisory biases and other external constraints are not placed on the analysis. In this Thesis, I present my work on expanding the scope and quality of single-particle tracking analysis and, using this new method, present my investigations of the dynamics involved in several complex biological questions in both prokaryotes and eukaryotes. Chapter 2 proposes a new analysis method for single-particle tracking data based on a nonparametric Bayesian statistical framework that we call SMAUG. The accuracy and precision of this method, as well as its ability to uncover the true dynamics, is investigated using realistic simulations and in vitro experimental systems. This new method increases the information available from tracking experiments while not sacrificing accuracy or precision, thus allowing for more rigorous conclusions. In addition, this method is also applied to in vivo data from two relevant biological systems and the analysis identifies potential biological roles for the uncovered diffusive states. Chapter 2 demonstrates a method for improving the scope of single-molecule analysis by introducing a new analysis framework that increases the information available. Differential gene expression patterns are the basis of cellular biology. The markers that modulate which genes are active and which are silenced are called epigenetic markers. In Chapter 3, I use single-particle tracking and the SMAUG algorithm to investigate the dynamics behind epigenetic silencing in a fission yeast model system and uncover the hidden complexity of the system. I present the dynamics uncovered for the key protein in the pathway, Swi6, in otherwise wild-type cells. This measurement resolves four distinct biochemical states. Then, using targeted mutation studies, I investigate and assign a biological role to each of the four identified states, and I uncover the impact that DNA compaction has upon the system. Overall, my application of single-particle tracking and SMAUG analysis to this system provides an example of expanding the scope of single-particle imaging techniques to complex systems and using the information obtained to gain biological insight. Bacterial virulence is a complex pathway that requires precise timing and organization of the proteins involved to effect a response. In Chapter 4, I present my investigations deeper into the dynamics of Vibrio cholerae bacterial. I found three distinct biological states for the keystone protein, TcpP , in otherwise wild-type cells. Using mutation studies, I present the biological roles for the states uncovered and discuss future investigations into the system using more mutation studies. The work presented in this Thesis will have broad impact on the fields of biophysics and cell biology by expanding the scope and quality of the information gathered of single-particle tracking experiments and by answering specific questions about the dynamics of several biological pathways.Subjects
Single-particle tracking Bayesian statistics Epigenetics bacterial virulence super-resolution microscopy
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