Electrical & Computer Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2765
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Item Cellular Pattern Quantication and Automatic Bench-marking Data-set Generation on confocal microscopy images(2010) Cui, Chi; JaJa, Joseph; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The distribution, directionality and motility of the actin fibers control cell shape, affect cell function and are different in cancer versus normal cells. Quantification of actin structural changes is important for further understanding differences between cell types and for elucidation the effects and dynamics of drug interactions. We propose an image analysis framework to quantify the F-actin organization patterns in response to different pharmaceutical treatments.The main problems addressed include which features to quantify and what quantification measurements to compute when dealing with unlabeled confocal microscopy images. The resultant numerical features are very effective to profile the functional mechanism and facilitate the comparison of different drugs. The analysis software is originally implemented in Matlab and more recently the most time consuming part in the feature extraction stage is implemented onto the NVIDIA GPU using CUDA where we obtain 15 to 20 speedups for different sizes of image. We also propose a computational framework for generating synthetic images for validation purposes. The validation for the feature extraction is done by visual inspection and the validation for quantification is done by comparing them with well-known biological facts. Future studies will further validate the algorithms, and elucidate the molecular pathways and kinetics underlying the F-actin changes. This is the first study quantifying different structural formations of the same protein in intact cells. Since many anti-cancer drugs target the cytoskeleton, we believe that the quantitative image analysis method reported here will have broad applications to understanding the mechanisms of candidate pharmaceutical.Item Model-Based Genomic/Proteomic Signal Processing in Cancer Diagnosis and Prediction(2007-07-20) Qiu, Peng; Liu, K. J. Ray; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years, high throughput measurement technologies (gene microarray, protein mass spectrum) have made it possible to simultaneously monitor the expression of thousands of genes or proteins. A topic of great interest is to study the difference of gene/protein expressions between normal and cancer subjects. In the literature, various data-driven methods have been proposed, i.e. clustering and machine learning methods. In this thesis, an alternative model-driven approach is proposed. The proposed dependence model focuses on the interactions among genes or proteins. We have shown that the dependence model is highly effective in the classification of normal and cancer data. Moreover, different from data-driven methods, the dependence model carries specific biological meanings, and it has the potential for the early prediction of cancer. The concept of dependence network is proposed based on the dependence model. The interactions and co-regulation relationships among genes or proteins are modeled by the dependence network, from which we are able to reliably identify biomarkers, important genes or proteins for cancer prediction and drug development. The analysis extends to cell cycle time-series, where one subject is measured at multiple time points during the cell cycle. Understanding the cell cycle will greatly improve our understanding of the mechanism of cancer development. In the cell cycle time-series, measurements are based on a population of cells which are supposed to be synchronized. However, continuous synchronization loss is observed due to the diversity of individual cell growth rates. Therefore, the time-series measurement is a distorted version of the single-cell expression. In this thesis, we propose a polynomial-model-based resynchronization scheme, which successfully removes the distortion. The time-series data is further analyzed to identify gene regulatory relationships. For the identification of regulatory relationships, existing literatures mainly study the relationship between several regulators and one regulated gene. In this thesis, we use the eigenvalue pattern of the dependence model to characterize several regulated genes, and propose a novel method that examines the relationship between several regulator and several regulated genes simultaneously.