Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
3 results
Search Results
Item Characterizing the Complex Spatial Patterns in Biological Systems(2015) Parker, Joshua; Losert, Wolfgang; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Spatial point patterns are ubiquitous in natural systems, from the patterns of raindrops on a sidewalk to the organization of stars in a galaxy. In cell biology, these patterns can represent the locations of fluorescently-labeled molecules inside or on the surface of cells, or even represent the centers of the cells themselves. These patterns arise due to the signaling activity of the cells which are mediated by a broad range of chemicals, and understanding this activity is vital to investigating these complex systems. Luckily, though each pattern is unique, the statistical properties of the patterns embed information about the underlying pattern formation process. In this work, I demonstrate techniques to characterize the complex spatial patterns found in unicellular systems. Using topologically-derived measures, I demonstrated a technique to automatically classify sets of point patterns into groups to identify changes in higher order statistical moments due to experimental variation. This technique utilizes functional principal component analysis (FPCA) on the Minkowski functionals of a secondary pattern formed by imposing disks on each point center. I demonstrate that this better classifies a range of point pattern sets, and then applied this technique to pattern sets representing membrane-bound proteins in human immune cells, showing that this procedure correctly identifies non-interacting proteins. Further, I demonstrate a simulation-based technique to diminish the statistical impact of large-scale pattern features. In protein patterns, these represent the effects of membrane ruffling during pattern formation. These features dominate correlation measures, obscuring any hint of nanoscale clustering. Using heterogeneous Poisson null models for each cell to re-normalize their pairwise correlation functions, I found that patterns of LAT proteins ("linker for the activation of T-cells") do indeed cluster, with a characteristic length-scale of approximately 500 nm. By performing clustering analysis at this length scale on both the LAT patterns and their respective null models, I found that clusters are most commonly dimers, but that this clustering is strongly diminished upon T-cell activation. This loss of clustering may be due to the presence of unlabeled molecules that have been recruited to the cell membrane to form complexes with LAT. I also investigate both molecular and cell-center patterns in Dictyostellium discoideum cells, which are a model organism for amoeboid motion and G-protein receptor-mediated chemotaxis. These cells migrate using "autocrine" signal relay in that they both secrete and sense the same chemoattractant, cyclic adenosine monophosphate (cyclic AMP or cAMP). They also secrete phosphodiesterases that degrade the chemoattractant. This leads to streaming patterns of cells towards aggregation centers, which serve as sites of sporulation. To study these cells, I demonstrate an image analysis technique that statistically infers the local population of fluorescently-labeled mRNA units in fluorescent images of self-aggregating cells. The images were of experiments where two particular mRNAs were labeled along with their respective proteins, the first being adenylyl cyclase A (ACA), a molecule involved in the production of cAMP. ACA itself has already been seen to accumulate at the back of migrating cells. The location of these molecules were compared to that of the locations of cyclic AMP receptor 1 (cAR1), which is the cell's mechanism for gradient sensing. Using my analysis technique, I found that statistically significant proportions of ACA mRNA preferentially locate towards the rear of migrating cells, an assymetry that was also found to identically correlate with the asymmetry of ACA itself. This asymmetry was not seen in cAR1 mRNA, which tends to distribute uniformly. Further, the asymmetry in ACA was most exaggerated in cells migrating at the rear of streams, with the approach to the local aggregate center diminishing leading to more uniformly distributed molecules. This may suggest that ACA is locally translated at the back of migrating cells, a result requiring further investigation. I then construct a computational migration model of D. discoideum chemotaxis and use it to investigate how the streaming phase is effected by cell-cell adhesion as well as by the global degradation of cAMP. To classify the dynamics of the model with respect to cell density and external chemical gradient, the two relevant phase variables, I develop an order parameter based on the fraction of broken cell-cell contacts over time. This parameter successfully classifies the dynamic steady states of the model (independent motion, streaming, and aggregation), outperforming the often used "chemotactic index". I found that the elimination of degradation strongly diminishes any presence of streaming, suggesting that chemical degradation is vital to stream formation. In contrast, the addition of cell-cell adhesion expanded the streaming phase, stabilizing streams that were formed initially through signal relay.Item Amoeboid Shape Dynamics on Flat and Topographically Modified Surfaces(2012) Driscoll, Meghan Katrien; Losert, Wolfgang; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)I present an analysis of the shape dynamics of the amoeba Dictyostelium discoideum, a model system for the study of cellular migration. To better understand cellular migration in complicated 3-D environments, cell migration was studied on simple 3-D surfaces, such as cliffs and ridges. D. discoideum interact with surfaces without forming mature focal adhesion complexes. The cellular response to the surface topography was characterized by measuring and looking for patterns in cell shape. Dynamic cell shape is a measure of the interaction between the internal biochemical state of a cell and its external environment. For D. discoideum migrating on flat surfaces, waves of high boundary curvature were observed to travel from the cell front to the cell back. Curvature waves are also easily seen in cells that do not adhere to a surface, such as cells that are electrostatically repelled from the coverslip or cells that are extended over the edge of micro-fabricated cliffs. At the leading edge of adhered cells, these curvature waves are associated with protrusive activity, suggesting that protrusive motion can be thought of as a wave-like process. The wave-like character of protrusions provides a plausible mechanism for the ability of cells to swim in viscous fluids and to navigate complex 3-D topography. Patterning of focal adhesion complexes has previously been implicated in contact guidance (polarization or migration parallel to linear topographical structures). However, significant contact guidance is observed in D. discoideum, which lack focal adhesion complexes. Analyzing the migration of cells on nanogratings of ridges spaced various distances apart, ridges spaced about 1.5 micrometers apart were found to guide cells best. Contact guidance was modeled as an interaction between wave-like processes internal to the cell and the periodicity of the nanograting. The observed wavelength and speed of the oscillations that best couple to the surface are consistent with those of protrusive dynamics. Dynamic sensing via actin or protrusive dynamics might then play a role in contact guidance.Item Individual and collective dynamics of chemotaxing cells(2011) McCann, Colin Patrick; Losert, Wolfgang; Parent, Carole A; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The study of the dynamics of interacting self-propelled entities is a growing area of physics research. This dissertation investigates individual and collective motion of the eukaryote Dictyostelium discoideum, a system amenable to signal manipulation, mathematical modeling, and quantitative analysis. In the wild, Dictyostelium survive adverse conditions through collective behaviors caused by secreting and responding to chemical signals. We explore this collective behavior on size scales ranging from subcellular biochemistry up to dynamics of thousands of communicating cells. To study how individual cells respond to multiple signals, we perform stability analysis on a previously-developed computational model of signal sensing. Polarized cells are linearly stable to perturbations, with a least stable region at about 60 degrees off the polarization axis. This finding is confirmed through simulations of the model response to additional chemical signals. The off-axis sensitivity suggests a mechanism for previously observed zig-zag motion of real cells randomly migrating or chemotaxing in a linear gradient. Moving up in scale, we experimentally investigate the rules of cell motion and interaction in the context of thousands of cells. Migrating Dictyostelium discoideum cells communicate by sensing and secreting directional signals, and we find that this process leads to an initial signal having an increased spatial range of an order of magnitude. While this process steers cells, measurements indicate that intrinsic cell motility remains unaffected. Additionally, migration of individual cells is unaffected by changing cell-surface adhesion energy by nine orders of magnitude, showing that individual motility is a robust process. In contrast, we find that collective dynamics depend on cell-surface adhesion, with greater adhesion causing cells to form smaller collective structures. Overall, this work suggests that the underlying migration ability of individual Dictyostelium cells operates largely independent of environmental conditions. Our gradient-sensing model shows that polarized cells are stable to small perturbations, and our experiments demonstrate that the motility apparatus is robust to considerable changes in cell-surface adhesion or complex signaling fields. However, we find that environmental factors can dramatically affect the collective behavior of cells, emphasizing that the laws governing cell-cell interaction can change migration patterns without altering intrinsic cell motility.