Pattern Formation and Organization of Epithelial Tissues

Developmental biology is a study of how elaborate patterns, shapes, and functions emerge as an organism grows and develops its body plan. From the physics point of view this is very much a self-organization process. The genetic blueprint contained in the DNA does not explicitly encode shapes and patterns an animal ought to make as it develops from an embryo. Instead, the DNA encodes various proteins which, among other roles, specify how different cells function and interact with each other. Epithelial tissues, from which many organs are sculpted, serve as experimentally- and analytically-tractable systems to study patterning mechanisms in animal development. Despite extensive studies in the past decade, the mechanisms that shape epithelial tissues into functioning organs remain incompletely understood. This thesis summarizes various studies we have done on epithelial organization and patterning, both in abstract theory and in close contact with experiments. A novel mechanism to establish cellular left-right asymmetry based on planar polarity instabilities is discussed. Tissue chirality is often assumed to originate from handedness of biological molecules. Here we propose an alternative where it results from spontaneous symmetry breaking of planar polarity mechanisms. We show that planar cell polarity (PCP), a class of well-studied mechanisms that allows epithelia to spontaneously break rotational symmetry, is also generically capable of spontaneously breaking reflection symmetry. Our results provide a clear interpretation of many mutant phenotypes, especially those that result in incomplete inversion. To bridge theory and experiments, we develop quantitative methods to analyze fluorescence microscopy images. Included in this thesis are algorithms to selectively project intensities from a surface in z-stack images, analysis of cells forming short chain fragments, analysis of thick fluorescent bands using steerable ridge detector, and analysis of cell recoil in laser ablation experiments. These techniques, though developed in the context of zebrafish retina mosaic, are general and can be adapted to other systems. Finally we explore correlated noise in morphogenesis of fly pupa notum. Here we report unexpected correlation of noise in cell movements between left and right halves of developing notum, suggesting that feedback or other mechanisms might be present to counteract stochastic noise and maintain left-right symmetry.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138800/1/hjeremy_1.pd

Live Cell Compartment Tracking: Object Tracking in Oscillating Intensity Images

Mathematical modeling has made great strides since the Lotka-Volterra predator-prey models. Newer models attempt to describe sub-cellular signal transduction pathways, such as the JAK-STAT and NF-κB pathways. However, the tools to accurately determine reaction and translocation rates in these pathways still have a number of drawbacks, including the effects of concentration scale on determining reaction rates and the effects of bulky additions to translocation rates. One method of overcoming these problems in signal transduction rate determination is to sample and stain cells from a full population at specific time points. However, fixed cell methods can only generate an average population rate. This could become an issue if the rate depends on the genotype of one of the proteins in the pathway. Another method of overcoming these problems in signal transduction rates is to use unmarked nuclei in live-cell imaging techniques. However, live cell imaging methods poses different problems, primarily how to find and track nuclei and cytoplasm when cells are actively moving and the nuclear and cytoplasmic intensities are by necessity fluctuating. To date, there is only one software package designed for tracking cells under these conditions - Cell Tracker (Shen et al., 2006). Cell Tracker is designed to handle the tracking of live cell images for protein translocation studies. They recommend using a separate color channel to mark the nucleus, although results can be obtained using unmarked nuclei. The results from Cell Tracker with unmarked nuclei are often less than optimal. We have developed a novel segmentation scheme and variation of the particle filter algorithm to allow more accurate tracking in time series with unmarked nuclei. The proposed segmentation scheme uses a non-parametric level set algorithm to refine a fast initial thresholding step. The tracking scheme uses a dense optical flow calculation to assist the particle filter algorithm in continuing to follow the true positions of the nuclei. To test the proposed algorithm, a novel mimicry of cell movement has been developed using random perturbations of a triangular mesh structure through the use of the finite element method

Liquid-Solid Transitions with Applications to Self-Assembly.

We study the thermodynamic and kinetic pathways by which liquids transform into solids, and their relation to the metastable states that commonly arise in self-assembly applications. As a case study in the formation of ordered metastable solids, we investigate the atomistic mechanism by which quasicrystals form. We show that the aperiodic growth of quasicrystals is controlled by the ability of the growing quasicrystal "nucleus" to incorporate kinetically trapped atoms into the solid phase with minimal rearrangement. In a related study, we propose a two-part mechanism for forming 3d dodecagonal quasicrystals by self-assembly. Our mechanism involves (1) attaching small mobile particles to the surface of spherical particles to encourage icosahedral packing and (2) allowing a subset of particles to deviate from the ideal spherical shape, to discourage close-packing. In addition to studying metastable ordered solids, we investigate the phenomenology and mechanism of the glass transition. We report measurements of spatially heterogeneous dynamics in a system of air-driven granular beads approaching a jamming transition, and show that the dynamics in our granular system are quantitatively indistinguishable from those for a supercooled liquid approaching a glass transition. In a second study of the glass transition, we use transition path sampling to study the structure, statistics and dynamics of localized excitations for several model glass formers. We show that the excitations are sparse and localized, and their size is temperature-independent. We show that their equilibrium concentration is proportional to exp[-Ja(1/T-1/To)], where "Ja" is the energy scale for irreversible particle displacements of length "a," and "To" is an onset temperature. We show that excitation dynamics is facilitated by the presence of other excitations, causing dynamics to slow in a hierarchical way as temperature is lowered. To supplement our studies of liquid-solid transitions, we introduce a shape matching framework for characterizing structural transitions in systems with complex particle shapes or morphologies. We provide an overview of shape matching methods, explore a particular class of metrics known as "harmonic descriptors," and show that shape matching methods can be applied to a wide range of nanoscale and microscale assembly applications.Ph.D.Chemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78931/1/askeys_1.pd

Two-dimensional Glasses

abstract: The structure of glass has been the subject of many studies, however some details remained to be resolved. With the advancement of microscopic imaging techniques and the successful synthesis of two-dimensional materials, images of two-dimensional glasses (bilayers of silica) are now available, confirming that this glass structure closely follows the continuous random network model. These images provide complete in-plane structural information such as ring correlations, and intermediate range order and with computer refinement contain indirect information such as angular distributions, and tilting. This dissertation reports the first work that integrates the actual atomic coordinates obtained from such images with structural refinement to enhance the extracted information from the experimental data. The correlations in the ring structure of silica bilayers are studied and it is shown that short-range and intermediate-range order exist in such networks. Special boundary conditions for finite experimental samples are designed so atoms in the bulk sense they are part of an infinite network. It is shown that bilayers consist of two identical layers separated by a symmetry plane and the tilted tetrahedra, two examples of added value through the structural refinement. Finally, the low-temperature properties of glasses in two dimensions are studied. This dissertation presents a new approach to find possible two-level systems in silica bilayers employing the tools of rigidity theory in isostatic systems.Dissertation/ThesisDoctoral Dissertation Physics 201

Cold Micro Metal Forming

This open access book contains the research report of the Collaborative Research Center “Micro Cold Forming” (SFB 747) of the University of Bremen, Germany. The topical research focus lies on new methods and processes for a mastered mass production of micro parts which are smaller than 1mm (by forming in batch size higher than one million). The target audience primarily comprises research experts and practitioners in production engineering, but the book may also be of interest to graduate students alike

Extracting Computational Representations of Place with Social Sensing

Place-based GIS are at the forefront of GIScience research and characterized by textual descriptions, human conceptualizations as well as the spatial-semantic relationships among places. The concepts of places are difficult to handle in geographic information science and systems because of their intrinsic vagueness. They arise from the complex interaction of individuals, society, and the environment. The exact delineation of vague regions is challenging as their borders are vague and the membership within a region varies non-monotonically and as a function of context. Consequently, vague regions are difficult to handle computationally, e.g., in spatial analysis, cartography, geographic information retrieval, and GIS workflows in general. The emergence of big data brings new opportunities for us to understand the place semantics from large-scale volunteered geographic information and data streams, such as geotags, texts, activity streams, and GPS trajectories. The term "social sensing" describes such individual-level big geospatial data and the associated analysis methods. In this dissertation, I present a generalizable, data-driven framework that complements classical top-down approaches by extracting the representations of vague cognitive regions and function regions from bottom-up approaches using spatial statistics and machine learning techniques with various social sensing sources. I demonstrate how to derive crisp boundaries for cognitive and functional regions from points of interest data, and show how natural language processing techniques can enrich our understanding of places and form a foundation for the semantic characterization of place types and the generalization of regions. This work makes contributions to the development of computational methodologies for extracting vague cognitive regions and functional regions using data-driven approaches as well as the novel semantic generalization processing technique