LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

LIPIcs, Volume 248, ISAAC 2022, Complete Volume

LIPIcs, Volume 248, ISAAC 2022, Complete Volum

Towards Smarter Fluorescence Microscopy: Enabling Adaptive Acquisition Strategies With Optimized Photon Budget

Fluorescence microscopy is an invaluable technique for studying the intricate process of organism development. The acquisition process, however, is associated with the fundamental trade-off between the quality and reliability of the acquired data. On one hand, the goal of capturing the development in its entirety, often times across multiple spatial and temporal scales, requires extended acquisition periods. On the other hand, high doses of light required for such experiments are harmful for living samples and can introduce non-physiological artifacts in the normal course of development. Conventionally, a single set of acquisition parameters is chosen in the beginning of the acquisition and constitutes the experimenter’s best guess of the overall optimal configuration within the aforementioned trade-off. In the paradigm of adaptive microscopy, in turn, one aims at achieving more efficient photon budget distribution by dynamically adjusting the acquisition parameters to the changing properties of the sample. In this thesis, I explore the principles of adaptive microscopy and propose a range of improvements for two real imaging scenarios. Chapter 2 summarizes the design and implementation of an adaptive pipeline for efficient observation of the asymmetrically dividing neurogenic progenitors in Zebrafish retina. In the described approach the fast and expensive acquisition mode is automatically activated only when the mitotic cells are present in the field of view. The method illustrates the benefits of the adaptive acquisition in the common scenario of the individual events of interest being sparsely distributed throughout the duration of the acquisition. Chapter 3 focuses on computational aspects of segmentation-based adaptive schemes for efficient acquisition of the developing Drosophila pupal wing. Fast sample segmentation is shown to provide a valuable output for the accurate evaluation of the sample morphology and dynamics in real time. This knowledge proves instrumental for adjusting the acquisition parameters to the current properties of the sample and reducing the required photon budget with minimal effects to the quality of the acquired data. Chapter 4 addresses the generation of synthetic training data for learning-based methods in bioimage analysis, making them more practical and accessible for smart microscopy pipelines. State-of-the-art deep learning models trained exclusively on the generated synthetic data are shown to yield powerful predictions when applied to the real microscopy images. In the end, in-depth evaluation of the segmentation quality of both real and synthetic data-based models illustrates the important practical aspects of the approach and outlines the directions for further research

Statistical problems arising from crystal structure analysis

This thesis is concerned with the application of statistical techniques in the field of crystallography - a branch of science dealing with the structure, classification and properties of crystals - and an analysis of some of the associated statistical problems. We shall concentrate throughout on the estimation of atomic co-ordinates within the unit cells of crystals. The science of X-ray crystallography will be introduced and a review of some of the existing methodology given. We shall then consider how statistical ideas may be used to improve this methodology. We shall be particularly concerned with the area of sequential experimentation, in which the data collection process itself is modified as a result of analysing the data already collected. Sequential experimentation for improved efficiency in any particular crystallographic problem requires that decisions be made as to which additional data should be collected in order to achieve the desired objective. Ways of selecting suitable sampling strategies will be described, together with associated stopping rules. We will also describe methods for handling relevant prior information - e.g. structural information available in crystallographic data bases - and nuisance parameters, and procedures for dealing with the inherent non-linearity of the crystallographic model, matrix updating and the recursive addition of data. The central problem of X-ray crystallography - the 'phase problem' - will also be analysed from a statistical perspective. Practical application of some of our ideas will be given. Much emphasis is placed on non-linear parameter estimation problems such as those arising in crystallography. A review of relevant statistical work in this general field is undertaken, and geometry-based ideas of our own proposed. We concentrate on either seeking suitable re-parameterisations (in a sense which we define) or on seeking alternatives to the standard tangent plane approximation to the solution surface based on relevant curvature measures. The thesis ends with a few relevant concluding comments and some ideas for further related statistical work in the area of X-ray crystallography

Self-organization in heterogeneous biological systems

Self-organization is an ubiquitous and fundamental process that underlies all living systems. In cellular organisms, many vital processes, such as cell division and growth, are spatially and temporally regulated by proteins -- the building blocks of life. To achieve this, proteins self-organize and form spatiotemporal patterns. In general, protein patterns respond to a variety of internal and external stimuli, such as cell shape or inhomogeneities in protein activity. As a result, the dynamics of intracellular pattern formation generally span multiple spatial and temporal scales. This thesis addresses the underlying mechanisms that lead to the formation of heterogeneous patterns. The main themes of this work are organized into three parts, which are summarized below. The first part deals with the general problem of mass-conserving reaction-diffusion dynamics in spatially non-uniform systems. In section 1 of chapter II, we study the dynamics of the E. coli Min protein system -- a paradigmatic model for pattern formation. More specifically, we consider a setup with a fixed spatial heterogeneity in a control parameter, and show that this leads to complex multiscale pattern formation. We develop a coarse-graining approach that enables us to explain and reduce the dynamics to the "hydrodynamic variables'' at large length and time scales. In another project, we consider a system where spatial heterogeneities are not imposed externally, but self-generated by the dynamics via a mechanochemical feedback loop between geometry and reaction-diffusion system (section 2 of chapter II). We show that the resulting dynamics can be explained from the phase-space geometry of the reaction-diffusion system. The second part focuses on how patterns in realistic cell geometries are controlled by shape and biochemical cues. We examine axis selection of PAR polarity patterns in C. elegans, where we show that spatial variations in the bulk-surface ratio and a tendency of the system to minimize the pattern interface yield robust long-axis polarization of PAR protein patterns (section 1 of chapter III). In a second project, we develop a theoretical model that explains the localization of the B. subtilis Min protein system (section 2 of chapter 3). We show that a biochemical cue -- which acts as a template for pattern formation -- guides and stabilizes Min patterns. In the third part, we study the coupling between lipid membranes and curvature-generating proteins. We demonstrate that myosin-VI motor proteins cooperatively bind to saddle-shaped regions of lipid membranes, and thereby induce large-scale membrane remodeling (section 1 of chapter IV). To understand the dynamics, we develop a coarse-grained geometric model and show that the emergence of regular spatial structures can be explained by a "push-pull'' mechanism: protein binding destabilizes the membrane shape at all length scales, and this is counteracted by line tension. Inspired by this system, we then investigate a general model for the dynamics of growing protein-lipid interfaces (section 2 of chapter IV). A key feature of the model is that the protein binding kinetics is explicitly coupled to the morphology of the interface. We show that such a coupling leads to turbulent dynamics and a roughening transition of the interface that is characterized by universal scaling behaviour