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- Genre: Masters Thesis
Description
The objective of this thesis was to compare various approaches for classification of the `good' and `bad' parts via non-destructive resonance testing methods by collecting and analyzing experimental data in the frequency and time domains. A Laser Scanning Vibrometer was employed to measure vibrations samples in order to determine the spectral characteristics such as natural frequencies and amplitudes. Statistical pattern recognition tools such as Hilbert Huang, Fisher's Discriminant, and Neural Network were used to identify and classify the unknown samples whether they are defective or not. In this work, a Finite Element Analysis software packages (ANSYS 13.0 and NASTRAN NX8.0) was used to obtain estimates of resonance frequencies in `good' and `bad' samples. Furthermore, a system identification approach was used to generate Auto-Regressive-Moving Average with exogenous component, Box-Jenkins, and Output Error models from experimental data that can be used for classification
ContributorsJameel, Osama (Author) / Redkar, Sangram (Thesis advisor) / Arizona State University (Publisher)
Created2013
Description
Phenotypic evolution is of great significance within biology, as it is the culmination of the influence of key evolutionary factors on the expression of genotypes. Deeper studies of the fundamental components, such as fitness effects of mutations and genetic variance within a population, allow one to predict the evolutionary trajectory of phenotypic evolution. In this regard, how much the change in mutational variance and the ongoing natural selection influence the rate of phenotypic evolution has yet to be fully understood. Therefore, this study measured mutational variances and the increasing rate of genetic variance during the experimental evolution of Escherichia coli populations, focusing on two growth-related traits, the populational maximum growth rate and carrying capacity. Mutational variances were measured by mutation-accumulation experiments, which allowed for the analysis of the effects of spontaneous mutations on growth-related traits in the absence of selection. This analysis revealed that some evolved populations developed a higher mutational variance for growth-related traits. Further investigation showed that most evolved populations have also developed a greater mutational effect, which could explain the increase in mutational variance. Finally, the genetic variances for most evolved populations are lower than expected in the absence of selection, and the involvement of either stabilizing or directional selection is evident. Future experiments with a larger sample size of experimentally evolved populations, as well as more intermediate timepoints during experimental evolution, may provide further insight regarding the complexities of the evolutionary outcomes of these traits.
ContributorsGonzales, Jadon (Author) / Lynch, Michael (Thesis advisor) / Geiler-Samerotte, Kerry (Committee member) / Ho, Wei-Chin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Protein misfolding is a problem faced by all organisms, but the reasons behind misfolded protein toxicity are largely unknown. It is difficult to pinpoint one exact mechanism as the effects of misfolded proteins can be widespread and variable between cells. To better understand their impacts, here I explore the consequences of misfolded proteins and if they affect all cells equally or affect some cells more than others. To investigate cell subpopulations, I built and optimized a cutting-edge single-cell RNA sequencing platform (scRNAseq) for yeast. By using scRNAseq, I can study the expression variability of many genes (i.e. how the transcriptomes of single cells differ from one another). To induce misfolding and study how single cells deal with this stress, I use engineered strains with varying degrees of an orthogonal misfolded protein. When I computationally cluster the cells expressing misfolded proteins by their sequenced transcriptomes, I see more cells with the severely misfolded protein in subpopulations undergoing canonical stress responses. For example, I see these cells tend to overexpress chaperones, and upregulate mitochondrial biogenesis and transmembrane transport. Both of these are hallmarks of the “Generalized” or “Environmental Stress Response” (ESR) in yeast. Interestingly, I do not see all components of the ESR upregulated in all cells, which may suggest that the massive transcriptional changes characteristic of the ESR are an artifact of having defined the ESR in bulk studies. Instead, I see some cells activate chaperones, while others activate respiration in response to stress. Another intriguing finding is that growth supporting proteins, such as ribosomes, have particularly heterogeneous expression levels in cells expressing misfolded proteins. This suggests that these cells potentially reallocate their metabolic functions at the expense of growth but not all cells respond the same. In sum, by using my novel single-cell approach, I have gleaned new insights about how cells respond to stress. which can help me better understand diseased cells. These results also teach how cells contend with mutation, which commonly causes protein misfolding and is the raw material of evolution. My results are the first to explore single-cell transcriptional responses to protein misfolding and suggest that the toxicity from misfolded proteins may affect some cells’ transcriptomes differently than others.
ContributorsEder, Rachel (Author) / Geiler-Samerotte, Kerry (Thesis advisor) / Brettner, Leandra (Committee member) / Wideman, Jeremy (Committee member) / Arizona State University (Publisher)
Created2023
Description
Single-cell DNA sequencing (scDNA-seq) can identify genetic differencesbetween individual cells and has broad applications in studying biology. For example, because scDNA-seq preserves haplotypes, it enables the addition of information about the fitness of different combinations of mutations into studies that quantify the fitness of individual mutations. However, it requires separating cells manually or using machinery, which is time-consuming and costly as every cell requires a separate reaction. Thus, most studies are limited to a few hundred cells, and scaling up is expensive and challenging. This problem also makes it difficult to multiplex samples or to study multiple sample types in the same experiment. To solve these problems, I introduce a novel method for sequencing DNA in heterogeneous cell populations by using the cell itself as a container for sequencing reactions, eliminating the need to isolate individual cells. The method involves diffusing DNA polymerase and barcoded primers into intact cells and amplifying its DNA Intracellularly. To ensure that DNA from each cell can be uniquely identified, I use combinatorial barcoding, which assigns a specific barcode to each cell using a unique combination of non-unique nucleotide block sequences. This allows for the pooling of cells, making the method multiplexable and enabling the analysis of dozens of samples containing thousands of cells. The method is flexible and allows for targeted sequencing of a region of interest and whole genome sequencing. I optimize the method for various organisms and applications so it can be made accessible to a wide range of research groups.
ContributorsCrossland, Parker Ella (Author) / Geiler-Samerotte, Kerry (Thesis advisor) / Wideman, Jeremy (Committee member) / Brettner, Leandra (Committee member) / Arizona State University (Publisher)
Created2024