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Computational biology is an interdisciplinary field that focuses on developing mathematical models and algorithms to interpret biological data so as to understand biological problems. With current high-throughput technology development, different types of biological data can be measured in a large scale, which calls for more sophisticated computational methods to analyze and interpret the data. In this dissertation research work, we propose novel methods to integrate, model and analyze multiple biological data, including microarray gene expression data, protein-DNA interaction data and protein-protein interaction data. These methods will help improve our understanding of biological systems. First, we propose a knowledge-guided multi-scale independent component analysis (ICA) method for biomarker identification on time course microarray data. Guided by a knowledge gene pool related to a specific disease under study, the method can determine disease relevant biological components from ICA modes and then identify biologically meaningful markers related to the specific disease. We have applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification. Second, we propose a novel method for transcriptional regulatory network identification by integrating gene expression data and protein-DNA binding data. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes increasingly significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to reduce false positive predictions by integrating binding motif information and gene expression data; a significance analysis procedure is followed to assess the significance of each regulatory module. The resulting performance on simulation data and yeast cell cycle data shows that the multi-level SVR approach outperforms other existing methods in the identification of both regulators and their target genes. We have further applied the proposed method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Third, we propose a bootstrapping Markov Random Filed (MRF)-based method for subnetwork identification on microarray data by incorporating protein-protein interaction data. Methodologically, an MRF-based network score is first derived by considering the dependency among genes to increase the chance of selecting hub genes. A modified simulated annealing search algorithm is then utilized to find the optimal/suboptimal subnetworks with maximal network score. A bootstrapping scheme is finally implemented to generate confident subnetworks. Experimentally, we have compared the proposed method with other existing methods, and the resulting performance on simulation data shows that the bootstrapping MRF-based method outperforms other methods in identifying ground truth subnetwork and hub genes. We have then applied our method to breast cancer data to identify significant subnetworks associated with drug resistance. The identified subnetworks not only show good reproducibility across different data sets, but indicate several pathways and biological functions potentially associated with the development of breast cancer and drug resistance. In addition, we propose to develop network-constrained support vector machines (SVM) for cancer classification and prediction, by taking into account the network structure to construct classification hyperplanes. The simulation study demonstrates the effectiveness of our proposed method. The study on the real microarray data sets shows that our network-constrained SVM, together with the bootstrapping MRF-based subnetwork identification approach, can achieve better classification performance compared with conventional biomarker selection approaches and SVMs. We believe that the research presented in this dissertation not only provides novel and effective methods to model and analyze different types of biological data, the extensive experiments on several real microarray data sets and results also show the potential to improve the understanding of biological mechanisms related to cancers by generating novel hypotheses for further study. / Ph. D.

Epigenetic modifications, such as DNA methylation and histone modifications, play important roles in gene expression and regulation, and are highly involved in cellular processes such as stem cell pluripotency/differentiation and tumorigenesis. Chromatin immunoprecipitation (ChIP) is the technique of choice for examining in vivo DNA-protein interactions and has been a great tool for studying epigenetic mechanisms. However, conventional ChIP assays require millions of cells for tests and are not practical for examination of samples from lab animals and patients. Automated microfluidic chips offer the advantage to handle small sample sizes and facilitate rapid reaction. They also eliminate cumbersome manual handling. In this report, I will talk about three different projects that utilized microfluidic immunoprecipitation followed by next genereation sequencing technologies to enable low input and high through epigenomics profiling. First, I examined RNA polymerase II transcriptional regulation with microfluidic chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) assays. Second, I probed the temporal dynamics in the DNA methylome during cancer development using a transgenic mouse model with microfluidic methylated DNA immunoprecipitation followed by next generation sequencing (MeDIP-seq) assays. Third, I explored negative enrichment of circulating tumor cells (CTCs) followed by microfluidic ChIP-seq technology for studying temporal dynamic histone modification (H3K4me3) of patient-derived tumor xenograft on an immunodeficient mouse model during the course of cancer metastasis. In the first study, I adapted microfluidic ChIP-seq devices to achieve ultrahigh sensitivity to study Pol2 transcriptional regulation from scarce cell samples. I dramatically increased the assay sensitivity to an unprecedented level (~50 K cells for pol2 ChIP-seq). Importantly, this is three orders of magnitude more sensitive than the prevailing pol2 ChIP-seq assays. I showed that MNase digestion provided better ChIP-seq signal than sonication, and two-steps fixation with MNase digestion provided the best ChIP-seq quality followed by one-step fixation with MNase digestion, and lastly, no fixation with MNase digestion. In the second study, I probed dynamic epigenomic changes during tumorigenesis using mice often require profiling epigenomes using a tiny quantity of tissue samples. Conventional epigenomic tests do not support such analysis due to the large amount of materials required by these assays. In this study, I developed an ultrasensitive microfluidics-based methylated DNA immunoprecipitation followed by next-generation sequencing (MeDIP-seq) technology for profiling methylomes using as little as 0.5 ng DNA (or ~100 cells) with 1.5 h on-chip process for immunoprecipitation. This technology enabled me to examine genome-wide DNA methylation in a C3(1)/SV40 T-antigen transgenic mouse model during different stages of mammary cancer development. Using this data, I identified differentially methylated regions and their associated genes in different periods of cancer development. Interestingly, the results showed that methylomic features are dynamic and change with tumor developmental stage. In the last study, I developed a negative enrichment of CTCs followed by ultrasensitive microfluidic ChIP-seq technology for profiling histone modification (H3K4Me3) of CTCs to resolve the technical challenges associated with CTC isolation and difficulties related with tools for profiling whole genome histone modification on tiny cell samples. / Ph. D.

In evolution alternative genetic trajectories can potentially lead to similar phenotypic outcomes. However, certain trajectories are preferred over others. These preferences bias the genomes of living organisms and the underlying processes can be observed in ongoing evolution. We have studied a variety of biases that can be found in bacterial chromosomes and determined the selective causes and functional consequences for the cell. We have quantified codon usage bias in highly expressed genes and shown that it is selected to optimise translational speed. We further demonstrated that the resulting differences in decoding speed can be used to regulate gene expression, and that the use of ‘non-optimal’ codons can be detrimental to reading frame maintenance. Biased gene location on the chromosome favours recombination between genes within gene families and leads to co-evolution. We have shown that such recombinational events can protect these gene families from inactivation by mobile genetic elements, and that chromosome organization can be selectively maintained because inversions can lead to the formation of unstable hybrid operons. We have used the development of antibiotic resistance to study how different bacterial lifestyles influence evolutionary trajectories. For this we used two distinct pairs of antibiotics and disease-causing bacteria, namely (i) Mycobacterium tuberculosis that is treated with rifampicin and (ii) Escherichia coli that is treated with ciprofloxacin. We have shown that in the slow-growing Mycobacterium tuberculosis, resistance mutations are selected for high-level resistance. Fitness is initially less important, and over time fitness costs can be ameliorated by compensatory mutations. The need for rapid growth causes the selection of ciprofloxacin resistance in Escherichia coli not only to be selected on the basis of high-level resistance but also on high fitness. Compensatory evolution is therefore not required and is not observed. Taken together, our results show that the evolution of a phenotype is the product of multiple steps and that many factors influence which trajectory is the most likely to occur and be most beneficial. Over time, selection will favour this particular trajectory and lead to biased evolution, affecting genome sequence and organization.

Herewithin, two transcription factor (TF) regulatory complexes were investigated. A bHLH–MYB–WDR (BMW) DNA-binding complex from maize was the first complex to be studied. R, a maize bHLH involved in the activation of genes in the anthocyanin pathway, had been characterized to indirectly bind DNA despite the presence of a functional DNA-binding domain. Findings presented here reveal that this is only partially correct. Direct DNA-binding by R was found to be dependent upon two distinct dimerization domains that function as a switch. This switch-like mechanism allows R to be repurposed for the activation of promoters of differing cis-element structure. The second regulatory complex studied was of the Arabidopsis thaliana MIKC-MADS TF family. For many TFs, DNA-binding site recognition is relatively straightforward and very sequence specific, while others exhibit relaxed sequence specificity. MADS-domain TFs are one family of TFs with a wider range of cis-element sequences. Though consensus cis-element sequences have been determined for various MADS-domains, correctly predicting and identifying biologically functional cis-elements has been a challenge. In order to study the influence of nucleobase associations within the cis-element, a DNA-Protein Interaction (DPI)-ELISA method was modified and optimized to screen a panel of specific probes. Screening of the SEP3 homodimer against a panel of sequential, palindromic probes revealed that nucleobases in position -1:+1 of the CArG-box influence binding strength between the MADS-domain and DNA. Additionally, the specificity of AGL15 towards CT-W6-AG forms was discovered to be determined by the functional groups present in the minor groove at position -4:+4 using inosine:cytosine (I:C) base pairs. Finally, the FLC–SVP MADS-domain heterodimer, bound to a native cis-element, was modeled and binding simulated using molecular dynamics. In conjunction with simulations of AGL15 and SEP3 homodimers, a potential binding mechanism was identified for this unique heterodimer. DNA sequence recognition by the MADS-domain was found to occur asymmetrically. In the case of the FLC–SVP heterodimer, the direction of asymmetrical DNA-binding in heterodimers was found to be fixed. Furthermore, the molecular dynamics simulations provided insight towards understanding the results generated from previous DPI-ELISA experiments, which should provide an improved means for predicting biologically significant CArG-boxes around genes.

La régulation transcriptionnelle des gènes est un processus indispensable sans lequel la diversité phénotypique des cellules ainsi que l’adaptation à leur environnement serait inexistant. L’identification des éléments de régulation dans le génome est d’une importance capitale afin de comprendre les mécanismes gouvernant l’expression des gènes spécifiques à un type cellulaire donné. Ainsi, suite au pic de LH, le follicule ovarien entre dans un programme intensif de différentiation cellulaire, orchestré par des modifications majeures du profile transcriptionnel des cellules de granulosa, déclenchant ultimement l’ovulation et la lutéinisation, processus indispensables à la fertilité femelle. L’hypothèse supportée par cette étude stipule qu’une réorganisation de la structure chromatinienne survient aux régions régulatrices d’une panoplie de gènes dans les heures suivant le pic de LH et qu’en isolant et identifiant ces régions, il serait possible de retrouver des éléments essentiels aux processus d’ovulation et de lutéinisation. Ainsi, en utilisant un protocole standard de superovulation chez la souris, les éléments de régulation se modifiant 4h suivant l’administration de hCG ont été isolés et identifiés dans les cellules de granulosa en utilisant la méthode FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) combinée à un séquençage haut débit. Cette étude a démontré que suite au stimulus ovulatoire, les cellules de granulosa subissent une reprogrammation majeure des éléments de régulation, qui est corrélée avec une modification drastique de leurs fonctions biologiques. De plus, cette étude a mis en évidence une association majoritaire des éléments de régulation à des régions intergéniques distales et à des introns, indiquant que ces régions ont une importance capitale dans la régulation transcriptionnelle dans les cellules de granulosa. Cette étude a également permis d’identifier une panoplie de régulateurs transcriptionnels reconnus pour être essentiels à la fonction ovarienne, ainsi que leur sites de liaison dans le génome, démontrant que la méthode FAIRE est une méthode assez puissante pour permettre la prédiction d’événements moléculaires précis ayant un sens physiologique réel. / Identification of regulatory elements in the genome is of paramount importance to understanding the mechanisms governing the expression of specific genes in a given cell type. Following the LH surge, the ovarian peri-ovulatory follicle enters an intensive program of cellular differentiation, orchestrated by major changes in the transcriptional profile of granulosa cells, ultimately triggering ovulation and luteinization, processes essentials for fertility in females. In the mouse, several genes essential to the success of this program are induced 2 to 6 hours after the ovulatory stimulus. Using a standard protocol for superovulation in mice, the regulatory elements were isolated and identified in granulosa cells 4h after administration of hCG using the method FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) combined with next generation sequencing. The results of this analysis demonstrate that after the ovulatory stimulus, granulosa cells undergo a major reprogramming of regulatory elements, which is correlated with the extensive changes in their biological functions. In addition, this study showed that most regulatory elements were associated with distal intergenic regions and introns, indicating that these regions are important in transcriptional regulation in granulosa cells. A variety of transcriptional regulators known to be essential for ovarian function, and their binding sites were also identified in this analysis, demonstrating that the FAIRE method has the power to predict molecular events that have correlates in the known physiology of ovarian processes.