| Description |
TITLE: EVALUATING AND OPTIMIZING THE PERFORMANCE OF REAL-TIME FEEDBACK-DRIVEN SINGLE PARTICLE TRACKING MICROSCOPES THROUGH THE LENS OF INFORMATION AND OPTIMAL CONTROL
ABSTRACT: Single particle tracking has become a ubiquitous class of tools in the study of biology at the molecular level. While the broad adoption of these techniques has yielded significant advances, it has also revealed the limitations of the methods. Most notable among these is that traditional single particle tracking is limited to imaging the particle at low temporal resolutions and small axial ranges. This restricts applications to slow processes confined to a plane. Biological processes in the cell, however, happen at multiple time scales and length scales. Real-time feedback-driven single particle tracking microscopes have emerged as one group of methods that can overcome these limitations. However, the development of these techniques has been ad-hoc and their performance has not been consistently analyzed in a way that enables comparisons across techniques, leading to incremental improvements on existing sets of tools, with no sense of fit or optimality with respect to SPT experimental requirements. This thesis addresses these challenges through three key questions : 1) What performance metrics are necessary to compare different techniques, allowing for easy selection of the method that best fits a particular application? 2) What is a procedure to design single particle tracking microscopes for the best performance?, and 3) How does one controllably and repeatably experimentally test single particle tracking performance on specific microscopes?. These questions are tackled in four thrusts: 1) a comprehensive review of real-time feedback-driven single particle tracking spectroscopy, 2) the creation of an optimization framework using Fisher information, 3) the design of a real-time feedback-driven single particle tracking microscope utilizing extremum seeking control, and 4) the development of synthetic motion, a protocol that provides biologically relevant known ground-truth particle motion to test single particle tracking microscopes and data analysis algorithms. The comprehensive review yields a unified view of single particle tracking microscopes and highlights two clear challenges, the photon budget and the control temporal budget, that work to limit the two key performance metrics, tracking duration and Fisher information. Fisher information provides a common framework to understand the elements of real-time feedback-driven single particle tracking microscopes, and the corresponding information optimization framework is a method to optimally design these microscopes towards an experimental aim. The thesis then expands an existing tracking algorithm to handle multiple particles through a multi-layer control architecture, and introduces REACTMIN, a new approach that reactively scans a minimum of light to overcome both the photon budget and the control temporal budget. This enables tracking durations up to hours, position localization down to a few nanometers, with temporal resolutions greater than 1 kHz. Finally, synthetic motion provides a repeatable and programmable method to test single particle tracking microscopes and algorithms with a known ground truth experiment. The performance of this method is analyzed in the presence of common actuator limitations.
COMMITTEE: ADVISOR Professor Sean Andersson, ME/SE; CHAIR Professor Calin Belta, ME/ECE/SE; Professor Roberto Tron, ME/SE; Professor Thomas Bifano, ME/BME/ECE/MSE; Professor Lei Tian, ECE/BME |
