A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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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.Item Impact of Stochasticity on Gene Regulation Networks(2007-05-21) Fang, Xin; Zafiriou, Evanghelos; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We studied the impact of stochasticity on gene regulation networks, using the cell-to-cell communication mechanism in Escherichia coli as an example. First we explored signal mediated positive autoregulation networks and their stochastic bistability, in the presence of which an initially homogeneous cell population would evolve into two distinct subpopulations. We proposed the simplification of the full network into one that can be theoretically studied. Simulation results indicate the simplifications retain the bistability and the distribution shapes so that the simplified network can be used to predict the bistable behavior of the full network. Moreover, it was shown that the bistability can be influenced by the signal molecule number, and that stochastic simulation is necessary for bistable systems. The self-promotion network for SdiA protein, with the autoinducer-2 (AI-2) signal molecule, was used as an example. The results further motivate the need for modeling of the AI-2 uptake mechanism. We next explored cell age distribution in the case where the number of a key protein for cell division has a stochastic bifurcation. With this bifurcation, the alive probability function (the probability that the cell has not divided) can be written in a double-exponential form. This analytical form allow the use of Laplace transform to calculate an analytical cell age distribution from the population balance model. The computation results indicate that if the key division protein number has a bifurcation, there is likely to be a significant fraction of first-generation cells in the cell population. Finally, we developed deterministic and stochastic models for the regulation network of the AI-2 uptake in Escherichia coli . This network is regulated by a set of lsr genes, and we proposed that the LsrD protein needs to reach a threshold for uptake to take place. Based on the deterministic model, kinetic parameter values were estimated by fitting to experimental data from the literature. During the step-by-step fitting procedure, data for mutant cells and effective data for wild type cells were used to avoid the complexity of the full wild-type network. With the estimated parameters, the deterministic simulation results matched experimental data well, except for a steep change and spike. A stochastic model was also developed and simulation results showed a mild change and no spike for the population means. The difference between stochastic means and deterministic paths is due to the LsrD protein number threshold and indicates that stochastic simulation may be necessary for a monostable system if it has a threshold mechanism.Item Time-Series Transcriptomic Analysis of a Systematically Perturbed Arabidopsis thaliana Liquid Culture System: A Systems Biology Perspective(2007-05-16) Dutta, Bhaskar; Klapa, Maria I; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Revealing the gene regulation network has been one of the main objectives of biological research. Studying such a complex, multi-scale and multi-parametric problem requires educated fingerprinting of cellular physiology at different molecular levels under systematically designed perturbations. Conventional biology lacked the means for holistic analysis of biological systems. In the post-genomic era, advances in robotics and biology lead to the development of high-throughput molecular fingerprinting technologies. Transcriptional profiling analysis using DNA microarrays has been the most widely used among them. My Ph.D. thesis concerns the dynamic, transcriptional profiling analysis of a systematically perturbed plant system. Specifically, Arabidopsis thaliana liquid cultures were subjected to three different stresses, i.e. elevated CO2 stress, salt (NaCl) stress and sugar (trehalose) applied individually, while the latter two stresses were also applied in combination with the CO2 stress. The transcriptional profiling of these conditions involved carrying out 320 microarray hybridizations, generating thus a vast amount of transcriptomic data for Arabidopsis thaliana liquid culture system. To upgrade the dynamic information content in the data, I developed a statistical analysis strategy that enables at each time point of a time-series the identification of genes whose expression changes in statistically significant amount due to the applied stress. Additional algorithms allow for further exploration of the dynamic significance analysis results to extract biologically relevant conclusions. All algorithms have been incorporated in a software suite called MiTimeS, written in C++. MiTimeS can be applied accordingly to analyze time-series data from any other high-throughput molecular fingerprint. The experimental design combined with existing multivariate statistical analysis techniques and MiTimeS revealed a wealth of biologically relevant dynamic information that had been unobserved before. Due to the high-throughput nature of the analysis, the study enabled the simultaneous identification and correlation of parallel-occurring phenomena induced by the applied stress. Stress responses comparisons indicated that transcriptional response of the biological system to combined stresses is usually not the cumulative effect of individual responses. In addition to the significance of the study for the analysis of the particular plant system, the experimental and analytical strategies used provide a systems biology methodological framework for any biological system, in general.