A study of computational methods to analyze gene expression data

The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.

In situ transmission electron microscopy ion irradiations of model iron-chromium alloys

Iron-chromium alloys are used as a model to study the microstructural evolution of defects in irradiated structural steel components of a nuclear reactor. We examine the effects of temperature and chromium concentration on the defect evolution and segregation behavior in the early stages of damage. In situ irradiations are conducted in a transmission electron microscope (TEM) at 300°C and 450°C with 150keV iron ions in single crystal Fe14Cr and Fe19Cr bicrystal to doses of 2E15 ions/cm^2. The microstructures resulting from annealing and irradiation of the alloy are characterized by analysis of TEM micrographs and diffraction patterns and compared with those of irradiated pure iron. We found the irradiation temperature to have little effect on the microstructural development. We also found that the presence of chromium in the sample leads to defect populations with small average loop size and no extended or nested loop structures, in contrast to the populations of large extended loops seen in irradiated pure iron. A very weak dependence was found on the specific chromium content of the alloy. Chromium was shown to suppress defect growth by inhibiting defect mobility in the alloy. While defects in pure iron are highly mobile and able to grow, those in the FeCr alloys remained small and relatively motionless due to the pinning effect of the chromium.