Master of Science / Department of Human Nutrition / Weiqun Wang / Weight control through either dietary calorie restriction (DCR) or exercise is associated with cancer prevention in animal models. However, the underlying mechanisms are not fully defined. Bioinformatics approaches using genomics, proteomics, and lipidomics were employed to elucidate the profiling changes of genes, proteins, and phospholipids in response to weight loss by DCR or exercise in a mouse skin cancer model. SENCAR mice were randomly assigned into 4 groups for 10 weeks: ad lib-fed sedentary control, ad lib-fed exercise (AE), exercise but pair-fed isocaloric amount of control (PE), and 20% DCR. Two hours after topical TPA treatment, skin epidermis was analyzed by Affymetrix for gene expression, DIGE for proteomics, and lipidomics for phospholipids. Body weights were significantly reduced in both DCR and PE but not AE mice versus the control. Among 39,000 transcripts, 411, 67, and 110 genes were significantly changed in DCR, PE, and AE, respectively. The expression of genes relevant to PI3K-Akt and Ras-MAPK signaling was effectively reduced by DCR and PE as measured through GenMAPP software. Proteomics analysis identified ~120 proteins, with 22 proteins significantly changed by DCR, including upregulated apolipoprotein A-1, a key antioxidant protein that decreases Ras-MAPK activity. Of the total 338 phospholipids analyzed by lipidomics, 57 decreased by PE including 5 phophatidylinositol species that serve as PI3K substrates. Although there were many impacts that we still need to characterize, it appears that both Ras-MAPK and PI3K-Akt signaling pathways are the key cancer preventive targets that have been consistently demonstrated by three bioinformatics approaches.

Doctor of Philosophy / Division of Biology / Ari M. Jumpponen / Microorganisms are critically important for establishing and maintaining ecosystem properties and processes that fuel and sustain higher-trophic levels. Despite the universal importance of microbes, we know relatively little about the rules and processes that dictate how microbial communities establish and assemble. Largely, we rely on assumptions that microbial community establishment follow similar trajectories as plants, but on a smaller scale. However, these assumptions have been rarely validated and when validation has been attempted, the plant-based theoretical models apply poorly to microbial communities. Here, I utilized genomics-inspired tools to interrogate microbial communities at levels near community saturation to elucidate the rules and patterns of microbial community assembly. I relied on a community filtering model as a framework: potential members of the microbial community are filtered through environmental and/or biotic filters that control which taxa can establish, persist, and coexist. Additionally, I addressed whether two different microbial groups (fungi and bacteria) share similar assembly patterns. Similar dispersal capabilities and mechanisms are thought to result in similar community assembly rules for fungi and bacteria. I queried fungal and bacterial communities along a deglaciated primary successional chronosequence to determine microbial successional dynamics and to determine if fungal and bacterial assemblies are similar or follow trajectories similar to plants. These experiments demonstrate that not only do microbial community assembly dynamics not follow plant-based models of succession, but also that fungal and bacterial community assembly dynamics are distinct. We can no longer assume that because fungi and bacteria share small propagule sizes they follow similar trends. Further, additional studies targeting biotic filters (here, snow algae) suggest strong controls during community assembly, possibly because of fungal predation of the algae or because of fungal utilization of algal exudates. Finally, I examined various technical aspects of sequence-based ecological investigations. These studies aimed to improve microbial community data reliability and analyses.

Doctor of Philosophy / Department of Computing and Information Sciences / Doina Caragea / Recent advancements in biological sciences have resulted in the availability of large amounts of sequence data (DNA and protein sequences). Biological sequence data can be annotated using machine learning techniques, but most learning algorithms require data to be represented by a vector of features. In the absence of biologically informative features, k-mers generated using a sliding window-based approach are commonly used to represent biological sequences. A larger k value typically results in better features; however, the number of k-mer features is exponential in k, and many k-mers are not informative. Feature selection is widely used to reduce the dimensionality of the input feature space. Most feature selection techniques use feature-class dependency scores to rank the features. However, when the amount of available labeled data is small, feature selection techniques may not accurately capture feature-class dependency scores. Therefore, instead of working with all k-mers, this dissertation proposes the construction of a reduced set of informative k-mers that can be used to represent biological sequences. This work resulted in three novel unsupervised approaches to construct features: 1. Burrows Wheeler Transform-based approach, that uses the sorted permutations of a given sequence to construct sequential features (subsequences) that occur multiple times in a given sequence. 2. Community detection-based approach, that uses a community detection algorithm to group similar subsequences into communities and refines the communities to form motifs (group of similar subsequences). Motifs obtained using the community detection-based approach satisfy the ZOMOPS constraint (Zero, One or Multiple Occurrences of a Motif Per Sequence). All possible unique subsequences of the obtained motifs are then used as features to represent the sequences. 3. Hybrid-based approach, that combines the Burrows Wheeler Transform-based approach and the community detection-based approach to allow certain mismatches to the features constructed using the Burrows Wheeler Transform-based approach. To evaluate the predictive power of the features constructed using the proposed approaches, experiments were conducted in three learning scenarios: supervised, semi-supervised, and domain adaptation for both nucleotide and protein sequence classification problems. The performance of classifiers learned using features generated with the proposed approaches was compared with the performance of the classifiers learned using k-mers (with feature selection) and feature hashing (another unsupervised dimensionality reduction technique). Experimental results from the three learning scenarios showed that features constructed with the proposed approaches were typically more informative than k-mers and feature hashing.

Master of Science / Department of Statistics / Susan J. Brown / Haiyan Wang / DNA hybridization microarray technologies have made it possible to gain an unbiased perspective of whole genome transcriptional activity on such a scale that is increasing more and more rapidly by the day. However, due to biologically irrelevant bias introduced by the experimental process and the machinery involved, correction methods are needed to restore the data to its true biologically meaningful state. Therefore, it is important that the algorithms developed to remove any sort of technical biases are accurate and robust. This report explores the concept of background correction in microarrays by using a real data set of five replicates of whole genome tiling arrays hybridized with genetic material from Tribolium castaneum. It reviews the literature surrounding such correction techniques and explores some of the more traditional methods through implementation on the data set. Finally, it introduces an alternative approach, implements it, and compares it to the traditional approaches for the correction of such errors.

Master of Science / Department of Statistics / Haiyan Wang / The selection of relevant genes for classification of phenotypes for diseases with gene expression data have been extensively studied. Previously, most relevant gene selection was conducted on individual gene with limited sample size. Modern technology makes it possible to obtain microarray data with higher resolution of the chromosomes. Considering gene sets on an entire block of a chromosome rather than individual gene could help to reveal important connection of relevant genes with the disease phenotypes. In this report, we consider feature selection and classification while taking into account of the spatial location of probe sets in classification of Duke’s stages B and C using DNA copy number data or gene expression data from colorectal cancers. A novel method was presented for feature selection in this report. A chromosome was first partitioned into blocks after the probe sets were aligned along their chromosome locations. Then a test of interaction between Duke’s stage and probe sets was conducted on each block of probe sets to select significant blocks. For each significant block, a new multiple comparison procedure was carried out to identify truly relevant probe sets while preserving the neighborhood location information of the probe sets. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification using the selected final probe sets was conducted for all samples. Leave-One-Out Cross Validation (LOOCV) estimate of accuracy is reported as an evaluation of selected features. We applied the method on two large data sets, each containing more than 50,000 features. Excellent classification accuracy was achieved by the proposed procedure along with SVM or KNN for both data sets even though classification of prognosis stages (Duke’s stages B and C) is much more difficult than that for the normal or tumor types.

Doctor of Philosophy / Department of Agronomy / Tesfaye Tesso / Fusarium stalk rot and charcoal rot caused by Fusarium thapsinum and Macrophomina phaseolina respectively are devastating global diseases in sorghum that lead to severe quality and yield loss each year. In this study, three sets of interrelated experiments were conducted that will potentially lead to the development of resistance based control option to these diseases. The first experiment was aimed at identifying sources of resistance to infection by M. phaseolina and F. thapsinum in a diverse panel of 300 sorghum genotypes. The genotypes were evaluated in three environments following artificial inoculation. Out of a total of 300 genotypes evaluated, 95 genotypes were found to have resistance to M. phaseolina and 77 to F. thapsinum of which 53 genotypes were resistant to both pathogens. In the second experiment, a set of 79,132 single nucleotide polymorphisms (SNPs) markers were used in an association study to identify genomic regions underlying stalk rot resistance using a multi-locus mixed model association mapping approach. We identified 14 loci associated with stalk rot and a set of candidate genes that appear to be involved in connected functions controlling plant defense response to stalk rot resistance. The associated SNPs accounted for 19-30% of phenotypic variation observed within and across environments. An analysis of associated allele frequencies within the major sorghum subpopulations revealed enrichment for resistant alleles in the durra and caudatum subpopulations compared with other subpopulations. The findings suggest a complicated molecular mechanism of resistance to stalk rots. The objective of the third experiment was to determine the functional relationship between stay-green trait, leaf dhurrin and soluble sugar levels and resistance to stalk rot diseases. Fourteen genotypic groups derived from a Tx642 × Tx7000 RIL population carrying combinations of stay-green quantitative trait loci were evaluated under three environments in four replications. The stg QTL had variable effects on stalk rot disease. Genotypes carrying stg1, stg3, stg1,3 and stg1,2,3,4 expressed good levels of resistance to M. phaseolina but the combination of stg1 and stg3 was required to express the same level of resistance to F. thapsinum. Other stg QTL blocks such as stg2 and stg4 did not have any impact on stalk rot resistance caused by both pathogens. There were no significant correlations between leaf dhurrin, soluble sugar concentration, and resistance to any of the pathogens.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / As genomes are sequenced, a major challenge is their annotation -- the identification of genes and regulatory elements, their locations and their functions. For years, it was believed that one gene corresponds to one protein, but the discovery of alternative splicing provided a mechanism for generating different gene transcripts (isoforms) from the same genomic sequence. In the recent years, it has become obvious that a large fraction of genes undergoes alternative splicing. Thus, understanding alternative splicing is a problem of great interest to biologists. Supervised machine learning approaches can be used to predict alternative splicing events at genome level. However, supervised approaches require large amounts of labeled data to produce accurate classifiers. While large amounts of genomic data are produced by the new sequencing technologies, labeling these data can be costly and time consuming. Therefore, semi-supervised learning approaches that can make use of large amounts of unlabeled data, in addition to small amounts of labeled data are highly desirable. In this work, we study the usefulness of a semi-supervised learning approach, co-training, for classifying exons as alternatively spliced or constitutive. The co-training algorithm makes use of two views of the data to iteratively learn two classifiers that can inform each other, at each step, with their best predictions on the unlabeled data. We consider three sets of features for constructing views for the problem of predicting alternatively spliced exons: lengths of the exon of interest and its flanking introns, exonic splicing enhancers (a.k.a., ESE motifs) and intronic regulatory sequences (a.k.a., IRS motifs). Naive Bayes and Support Vector Machine (SVM) algorithms are used as based classifiers in our study. Experimental results show that the usage of the unlabeled data can result in better classifiers as compared to those obtained from the small amount of labeled data alone. In addition to semi-supervised approaches, we also also study the usefulness of graph based transductive learning approaches for predicting alternatively spliced exons. Similar to the semi-supervised learning algorithms, transductive learning algorithms can make use of unlabeled data, together with labeled data, to produce labels for the unlabeled data. However, a classification model that could be used to classify new unlabeled data is not learned in this case. Experimental results show that graph based transductive approaches can make effective use of the unlabeled data.

Doctor of Philosophy / Department of Biochemistry / Subbarat Muthukrishnan / The extracellular matrices of cuticle and peritrophic matrix of insects are composed mainly of chitin complexed with proteins, some of which contain chitin-binding domains. This study is focused on the identification and functional characterization of genes encoding proteins that possess one or more copies of the six-cysteine-containing ChtBD2 domain (Peritrophin A motif =CBM_14 =Pfam 01607) in the red flour beetle, Tribolium castaneum. A bioinformatics search of T. castaneum genome yielded previously characterized chitin metabolic enzymes and several additional proteins. Using phylogenetic analyses, the exon-intron organization of the corresponding genes, domain organization of proteins, and temporal and tissue-specificity of expression patterns, these proteins were classified into three large families. The first family includes 11 proteins essentially made up of 1 to 14 repeats of the peritrophin A domain. Transcripts for these proteins are expressed only in the midgut and only during feeding stages of development. We therefore denote these proteins as “Peritrophic Matrix Proteins” or PMPs. The genes of the second and third families are expressed in cuticle-forming tissues throughout all stages of development but not in the midgut. These two families have been denoted as “Cuticular Proteins Analogous to Peritrophins 3” or CPAP3s and “Cuticular Proteins Analogous to Peritophins 1” or CPAP1s based on the number of ChtBD2 domains that they contain. Unlike other cuticular proteins studied so far, TcCPAP1-C protein is localized predominantly in the exocuticle and could contribute to the unique properties of this cuticular layer. RNA interference (RNAi), which down-regulates transcripts for any targeted gene, results in lethal and/or abnormal phenotypes for some, but not all, of these genes. Phenotypes are often unique and are manifested at different developmental stages, including embryonic, pupal and/or adult stages. The experiments presented in this dissertation reveal that while the vast majority of the CPAP3 genes serve distinct and essential functions affecting survival, molting or normal cuticle development. However, a minority of the CPAP1 and PMP family genes are indispensable for survival under laboratory conditions. Some of the non-essential genes may have functional redundancy or may be needed only under special circumstances such as exposure to stress or pathogens.

Master of Science / Department of Electrical and Computer Engineering / Sanjoy Das / Stephen M. Welch / Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.