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Biological systems constantly evolve and adapt in response to changed environment and external stimuli at the molecular and genomic levels. Building statistical models that characterize such dynamic changes in biological systems is one of the key objectives in bioinformatics and computational biology. Recent advances in high-throughput genomic and molecular profiling technologies such as gene expression and and copy number microarrays provide ample opportunities to study cellular activities at the individual gene and network levels. The aim of this dissertation is to formulate mathematically dynamic changes in biological networks and DNA copy numbers, to develop machine learning algorithms to learn these statistical models from high-throughput biological data, and to demonstrate their applications in systems biological studies. The first part (Chapters 2-4) of the dissertation focuses on the dynamic changes taking placing at the biological network level. Biological networks are context-specific and dynamic in nature. Under different conditions, different regulatory components and mechanisms are activated and the topology of the underlying gene regulatory network changes. We report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. Further, we formalize and extend the DDN approach to an effective learning strategy to extract structural changes in graphical models using l1-regularization based convex optimization. We discuss the key properties of this formulation and introduce an efficient implementation by the block coordinate descent algorithm. Another type of dynamic changes in biological networks is the observation that a group of genes involved in certain biological functions or processes coordinate to response to outside stimuli, producing distinct time course patterns. We apply the echo stat network, a new architecture of recurrent neural networks, to model temporal gene expression patterns and analyze the theoretical properties of echo state networks with random matrix theory. The second part (Chapter 5) of the dissertation focuses on the changes at the DNA copy number level, especially in cancer cells. Somatic DNA copy number alterations (CNAs) are key genetic events in the development and progression of human cancers, and frequently contribute to tumorigenesis. We propose a statistically-principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately detect genomic deletion type, estimate normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. / Ph. D.

DNA copy number change is an important form of structural variation in human genome. Somatic copy number alterations (CNAs) can cause over expression of oncogenes and loss of tumor suppressor genes in tumorigenesis. Recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale, with high resolution. Quantitative analysis of somatic CNAs on genes has found broad applications in cancer research. Most tumors exhibit genomic instability at chromosome scale as a result of dynamically accumulated genomic mutations during the course of tumor progression. Such higher level cancer genomic characteristics cannot be effectively captured by the analysis of individual genes. We introduced two definitions of chromosome instability (CIN) index to mathematically and quantitatively characterize genome-wide genomic instability. The proposed CIN indices are derived from detected CNAs using circular binary segmentation and wavelet transform, which calculates a score based on both the amplitude and frequency of the copy number changes. We generated CIN indices on ovarian cancer subtypes' copy number data and used them as features to train a SVM classifier. The experimental results show promising and high classification accuracy estimated through cross-validations. Additional survival analysis is constructed on the extracted CIN scores from TCGA ovarian cancer dataset and showed considerable correlation between CIN scores and various events and severity in ovarian cancer development. Currently our methods have been integrated into G-DOC. We expect these newly defined CINs to be predictors in tumors subtype diagnosis and to be a useful tool in cancer research. / Master of Science

DNA microarray has become a powerful tool in genetics, molecular biology, and biomedical research. DNA microarray can be used for measuring the genotypes, structural changes, and gene expressions of human genomes. Detection and characterization of multilevel, high-throughput microarray genomic data pose new challenges to statistical pattern recognition and machine learning research. In this dissertation, we propose novel computational methods for analyzing DNA copy number changes and learning the trees of phenotypes using DNA microarray data. DNA copy number change is an important form of structural variations in human genomes. The copy number signals measured by high-density DNA microarrays usually have low signal-to-noise ratios and complex patterns due to inhomogeneous composition of tissue samples. We propose a robust detection method for extracting copy number changes in a single signal profile and consensus copy number changes in the signal profiles of a population. We adapt a solution-path algorithm to efficiently solve the optimization problems associated with the proposed method. We tested the proposed method on both simulation and real CGH and SNP microarray datasets, and observed competitively improved performance as compared to several widely-adopted copy number change detection methods. We also propose a chromosome instability measure to summarize the extracted copy number changes for assessing chromosomal instabilities of tumor genomes. The proposed measure demonstrates distinct patterns between different subtypes of ovarian serous carcinomas and normal samples. Among active research on complex human diseases using genomic data, little effort and progress have been made in discovering the relational structural information embedded in the molecular data. We propose two stability analysis based methods to learn stable and highly resolved trees of phenotypes using microarray gene expression data of heterogeneous diseases. In the first method, we use a hierarchical, divisive visualization approach to explore the tree of phenotypes and a leave-one-out cross validation to select stable tree structures. In the second method, we propose a node bandwidth constraint to construct stable trees that can balance the descriptive power and reproducibility of tree structures. Using a top-down merging procedure, we modify the binary tree structures learned by hierarchical group clustering methods to achieve a given node bandwidth. We use a bootstrap based stability analysis to select stable tree structures under different node bandwidth constraints. The experimental results on two microarray gene expression datasets of human diseases show that the proposed methods can discover stable trees of phenotypes that reveal the relationships between multiple diseases with biological plausibility. / Ph. D.

<p>The array-based form of comparative genomic hybridization (array-CGH) is a new methodology that has shown to be of significant importance. This thesis focuses on the development of array-CGH with the aim to define candidate regions/genes on chromosome 22 in a wide spectrum of cancer-related conditions. In <b>paper I</b>, we developed and applied the first comprehensive genomic microarray, representing human chromosome 22, for analysis of DNA copy number. Using this array-based approach, we identified gene copy number alterations, including heterozygous/homozygous deletions, amplifications, IGLV/IGLC locus instability and the breakpoints of imbalanced translocation, in several 22q-associated disorders. In <b>paper II</b>, we applied the same array to perform DNA copy number profiling of a series of ovarian carcinoma. cDNA arrays were also used in this study to correlate gene expression levels with DNA-copy number. In the course of this analysis, we determined a small 3.5 Mb candidate 22q telomeric region and suggested a number of specific candidate genes. <b>Paper III</b> described the comprehensive and high-resolution analysis of chromosome 22 in a large set of various stage breast cancers. Multiple distinct patterns of genetic aberrations were observed. The smallest identified candidate locus was 220 kb in size and mapped to a gene-rich region in the vicinity of telomere of 22q. Intriguing result of this study was the detection of high frequency (26.6%) of intra-tumoral clonal variation in gene copy number profiles, which should be viewed as a high number, considering that we study in detail only a single human chromosome. In <b>paper IV</b>, we profiled a series of 28 Wilms tumor samples using 22q-array in order to assess specific regions affected with DNA dosage-alterations. The distribution of aberrations defined a complex amplifier genotype and delimited two tumor suppressor/oncogene candidate loci. These results open up for several avenues for continued research of these tumor forms. These findings also demonstrate the power of array-CGH in the precise determination of minute DNA copy number alterations and strengthen the notion that further studies, preferentially in the context of the entire human genome, are needed.</p>

The array-based form of comparative genomic hybridization (array-CGH) is a new methodology that has shown to be of significant importance. This thesis focuses on the development of array-CGH with the aim to define candidate regions/genes on chromosome 22 in a wide spectrum of cancer-related conditions. In <b>paper I</b>, we developed and applied the first comprehensive genomic microarray, representing human chromosome 22, for analysis of DNA copy number. Using this array-based approach, we identified gene copy number alterations, including heterozygous/homozygous deletions, amplifications, IGLV/IGLC locus instability and the breakpoints of imbalanced translocation, in several 22q-associated disorders. In <b>paper II</b>, we applied the same array to perform DNA copy number profiling of a series of ovarian carcinoma. cDNA arrays were also used in this study to correlate gene expression levels with DNA-copy number. In the course of this analysis, we determined a small 3.5 Mb candidate 22q telomeric region and suggested a number of specific candidate genes. <b>Paper III</b> described the comprehensive and high-resolution analysis of chromosome 22 in a large set of various stage breast cancers. Multiple distinct patterns of genetic aberrations were observed. The smallest identified candidate locus was 220 kb in size and mapped to a gene-rich region in the vicinity of telomere of 22q. Intriguing result of this study was the detection of high frequency (26.6%) of intra-tumoral clonal variation in gene copy number profiles, which should be viewed as a high number, considering that we study in detail only a single human chromosome. In <b>paper IV</b>, we profiled a series of 28 Wilms tumor samples using 22q-array in order to assess specific regions affected with DNA dosage-alterations. The distribution of aberrations defined a complex amplifier genotype and delimited two tumor suppressor/oncogene candidate loci. These results open up for several avenues for continued research of these tumor forms. These findings also demonstrate the power of array-CGH in the precise determination of minute DNA copy number alterations and strengthen the notion that further studies, preferentially in the context of the entire human genome, are needed.