Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput measurement devices are available, such as microarrays and sequencing techniques, regulatory networks have been intensively studied over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing regression models and apply them to construct regulatory networks which span trancription factors and microRNAs. We also propose an extended algorithm to address the local optimum issue in finding the Maximum A Posterjorj estimator. An E. coli mRNA expression microarray data set with known bona fide interactions is used to evaluate our models and we show that our regression networks with a properly chosen prior can perform comparably to the state-of-the-art regulatory network construction algorithm. Finally, we apply our models on a p53-related data set, NCI-60 data. By further incorporating available prior structural information from sequencing data, we identify several significantly enriched interactions with cell proliferation function. In both of the two data sets, we select specific examples to show that many regulatory interactions can be confirmed by previous studies or functional enrichment analysis. Through comparing statistical models, we conclude from the project that combining different models with over-representation analysis and prior structural information can improve the quality of prediction and facilitate biological interpretation. Keywords: regulatory network, variable selection, penalized maximum likelihood estimation, optimization, functional enrichment analysis.

We used mouse embryonic stem cells (ESCs) as a model to study the mechanisms that regulate stem cell fate. Using gene expression analysis during a time course of differentiation, we identified 281 candidate regulators of ESC fate. To integrate these candidate regulators into the known ESC transcriptional network, we incorporated promoter occupancy data for OCT4, NANOG and SOX2. We used shRNA knockdown studies followed by a high-content fluorescence imaging assay to test the requirement of our predicted regulators in maintaining self-renewal. We further integrated promoter occupancy data for Polycomb group (PcG) proteins, EED and PHC1 to identify 43 transcriptional networks in which we predict that OCT4 and NANOG co-operate with EED and PHC1 to influence the expression of multiple developmental regulators. Next, we turned our focus to the PcG protein PCL2 which we identified as being bound by both OCT4 and NANOG and down-regulated during differentiation. PcG proteins are conserved epigenetic transcriptional repressors that control numerous developmental gene expression programs. Using multiple biochemical strategies, we demonstrated that PCL2 associates with Polycomb Repressive Complex 2 (PRC2) in mouse ESCs, a complex that exerts its effect on gene expression through H3K27me3. Although PCL2 was not required for global histone methylation, it was required at specific target regions to maintain proper levels of H3K27me3. Knockdown of Pcl2 in ESCs resulted in heightened self-renewal characteristics and defects in differentiation. Integration of global gene expression and promoter occupancy analyses allowed us to identify PCL2 and PRC2 transcriptional targets and draft regulatory networks. We describe the role of PCL2 in both modulating transcription of ESC self-renewal genes in undifferentiated ESCs as well as developmental regulators during early commitment and differentiation.

We used mouse embryonic stem cells (ESCs) as a model to study the mechanisms that regulate stem cell fate. Using gene expression analysis during a time course of differentiation, we identified 281 candidate regulators of ESC fate. To integrate these candidate regulators into the known ESC transcriptional network, we incorporated promoter occupancy data for OCT4, NANOG and SOX2. We used shRNA knockdown studies followed by a high-content fluorescence imaging assay to test the requirement of our predicted regulators in maintaining self-renewal. We further integrated promoter occupancy data for Polycomb group (PcG) proteins, EED and PHC1 to identify 43 transcriptional networks in which we predict that OCT4 and NANOG co-operate with EED and PHC1 to influence the expression of multiple developmental regulators. Next, we turned our focus to the PcG protein PCL2 which we identified as being bound by both OCT4 and NANOG and down-regulated during differentiation. PcG proteins are conserved epigenetic transcriptional repressors that control numerous developmental gene expression programs. Using multiple biochemical strategies, we demonstrated that PCL2 associates with Polycomb Repressive Complex 2 (PRC2) in mouse ESCs, a complex that exerts its effect on gene expression through H3K27me3. Although PCL2 was not required for global histone methylation, it was required at specific target regions to maintain proper levels of H3K27me3. Knockdown of Pcl2 in ESCs resulted in heightened self-renewal characteristics and defects in differentiation. Integration of global gene expression and promoter occupancy analyses allowed us to identify PCL2 and PRC2 transcriptional targets and draft regulatory networks. We describe the role of PCL2 in both modulating transcription of ESC self-renewal genes in undifferentiated ESCs as well as developmental regulators during early commitment and differentiation.

Gene regulatory network (GRN) consists of a set of genes and regulatory relationships between the genes. As outputs of the GRN, gene expression data contain important information that can be used to reconstruct the GRN to a certain degree. However, the reverse engineer of GRNs from gene expression data is a challenging problem in systems biology. Conventional methods fail in inferring GRNs from gene expression data because of the relative less number of observations compared with the large number of the genes. The inherent noises in the data make the inference accuracy relatively low and the combinatorial explosion nature of the problem makes the inference task extremely difficult. This study aims at reconstructing the GRNs from time-course gene expression data based on GRN models using system identification and parameter estimation methods. The main content consists of three parts: (1) a review of the methods for reverse engineering of GRNs, (2) reverse engineering of GRNs based on linear models and (3) reverse engineering of GRNs based on a nonlinear model, specifically S-systems. In the first part, after the necessary background and challenges of the problem are introduced, various methods for the inference of GRNs are comprehensively reviewed from two aspects: models and inference algorithms. The advantages and disadvantages of each method are discussed. The second part focus on inferring GRNs from time-course gene expression data based on linear models. First, the statistical properties of two sparse penalties, adaptive LASSO and SCAD, with an autoregressive model are studied. It shows that the proposed methods using these two penalties can asymptotically reconstruct the underlying networks. This provides a solid foundation for these methods and their extensions. Second, the integration of multiple datasets should be able to improve the accuracy of the GRN inference. A novel method, Huber group LASSO, is developed to infer GRNs from multiple time-course data, which is also robust to large noises and outliers that the data may contain. An efficient algorithm is also developed and its convergence analysis is provided. The third part can be further divided into two phases: estimating the parameters of S-systems with system structure known and inferring the S-systems without knowing the system structure. Two methods, alternating weighted least squares (AWLS) and auxiliary function guided coordinate descent (AFGCD), have been developed to estimate the parameters of S-systems from time-course data. AWLS takes advantage of the special structure of S-systems and significantly outperforms one existing method, alternating regression (AR). AFGCD uses the auxiliary function and coordinate descent techniques to get the smart and efficient iteration formula and its convergence is theoretically guaranteed. Without knowing the system structure, taking advantage of the special structure of the S-system model, a novel method, pruning separable parameter estimation algorithm (PSPEA) is developed to locally infer the S-systems. PSPEA is then combined with continuous genetic algorithm (CGA) to form a hybrid algorithm which can globally reconstruct the S-systems.

A primary aim of Synthetic Biology is the design and implementation of biological systems that perform engineered functions. However, the assembly of double-stranded DNA molecules is a major barrier to this progress, as it remains time consuming and laborious. Here I present three improved methods for DNA assembly. The first is based on, and makes use of, BioBricks. The second method relies on overlap-extension PCR to assemble non-standard parts. The third method improves upon overlap extension PCR by reducing the number of steps and the time it takes to assemble DNA. Finally, I show how the PCR-based assembly methods presented here can be used, in concert, with in vivo homologous recombination in yeast to assemble as many as 19 individual DNA parts in one step. These methods will also be used to assemble an incoherent feedforward loop, gene regulatory network.

Modelling interactions between genes and their regulators is fundamental to understanding how, for example a disease progresses, or the impact of inserting a synthetic circuit into a cell. We use an existing method to infer regulatory networks under multiple conditions: the Joint Graphical Lasso (JGL), a shrinkage based Gaussian graphical model. We apply this method to two data sets: one, a publicly available set of microarray experiments perturbing the gram-positive bacteria Bacillus subtilis under multiple experimental conditions; the second, a set of RNA-seq samples of Mouse (Mus musculus) embryonic fibroblasts (MEFs) infected with different strains of the parasite Toxoplasma gondii. In both cases we infer a subset of the regulatory networks using relatively small sample sizes. For the Bacillus subtilis analysis we focused on the use of these regulatory networks in synthetic biology and found examples of transcriptional units active only under a subset of conditions, this information can be useful when designing circuits to have condition dependent behaviour. We developed methods for large network decomposition that made use of the condition information and showed a greater specificity of identifying single transcriptional units from the larger network using our method. Through annotating these results with known information we were able to identify novel connections and found supporting evidence for a selection of these from publicly available experimental results. Biological data collection is typically expensive and due to the relatively small sample sizes of our MEF data set we developed a novel empirical Bayes method for reducing the false discovery rate when estimating block diagonal covariance matrices. Using these methods we were able to infer regulatory networks for the host infected with either the ME49 or RH strain of the parasite. This enabled the identification of known and novel regulatory mechanisms. The Toxoplasma gondii parasite has shown to subvert host function using similar mechanisms as cancers and through our analysis we were able to identify genes, networks and ontologies associated with cancer, including connections that have not previously been associated with T. gondii infection. Finally a Shiny application was developed as an online resource giving access to the Bacillus subtilis inferred networks with interactive methods for exploring the networks including expansion of sub networks and large network decomposition.

The relationship between a pathogen and its host is a complex series of events that occurs at the molecular level and is controlled by transcriptional and protein interactions. To facilitate the understanding of these mechanisms in Aspergillus flavus and Zea mays, three approaches were taken: 1) the development of a predicted interactome for Z. mays (PiZeaM), 2) the development of co-expression networks for Z. mays and A. flavus from RNA-seq data, and 3) the development of causal inference networks depicting interactions between the host and the pathogen. PiZeaM is the genome-wide roadmap of protein-protein interactions that occur within Z. mays. PiZeaM helps create a novel map of the interactions in Z. mays in response to biotic and abiotic stresses. To further support the predicted interactions, an analysis of microarray-based gene expression was used to produce a gene co-expression network. PiZeaM was able to capture conserved resistance pathways involved involved in the response to pathogens, abiotic stress and development. Gene Co-expression networks were developed by the simultaneous use of correlations to develop networks for differentially expressed genes, resistance marker genes, pathogenicity genes, and genes involved is secondary metabolism in Z. mays and A. flavus. From these networks, correlation and anti-correlation of host and pathogen gene expression was detected, revealing genes that potentially interact at different stages of pathogenesis. Finally, causal gene regulatory relationships were inferred using partial correlation analysis of Z. mays infected with A. flavus over a 3 day period. The gene regulatory network (GRN) sheds light on the specifics of the mechanisms of pathogenesis and resistance that govern the Z. mays-A. flavus interaction. The direct product of this research is the understanding of key transcription factors and signaling genes involved in resistance. This body of research highlights how PPIs and GRNs can be utilized to identify biomarkers and gene functions in both Z. mays and A. flavus.

In nature, biofilm is a complex structure resulted of multicellular bacterial communities that provide important nutritional functions and the acquisition of protective traits such as antibiotics resistance and horizontal gene transfer. The development from the planktonic, lonely bacteria, to the mature multilayered biofilm structure consists of three main phases: motility, attachment and biofilm maturation. At cellular level, the process is controlled by several genes such as flhD, fliA, rpoS, csgD, adrA, cpxR all acting as master regulators. Additionally, the global regulators CRP, IHF, Fis, and others in less frequency, have been related to biofilm formation, although blurry information has been provided. In this thesis we used synthetic, molecular and cellular biology approaches to understand the effect of CRP, IHF and Fis in the transcriptional regulatory network in the bacterium Escherichia coli. In the first chapter, we employed network analysis to reconstruct and analyze part of the entire regulatory network described to modulate the flagella-biofilm program. With this analysis we identified some critical interactions responsible for the planktonic-biofilm transition. Next, we selected the top ten effectors nodes of the network and cloned the promoter region of those genes in a reporter system. As extensively explained in chapter II, this system allowed us to validate as well as suggest new interactions in the network. Additionally, the measurement of the promoter activity during bacterial development show that CRP, IHF and Fis differentially modulate most of the surveyed genes suggesting that those Global Regulators participate to modulate gene expression in different phases of the planktonic-biofilm development. At chapter three, to get a better overview of the entire process, we performed motility, adherence/early biofilm and mature biofilm assays. We describe the intrinsic ability of E. coli to perform motility, adherence and mature biofilm at 37?C. In contrast, the absence of ihf, fis as well as Carbon Catabolite Repression (CCR), lead to altered phenotypes at both motility and biofilm development. At the end, we discussed how the changes of promoter activity of target genes, together with our network analysis, could explain part of the altered phenotypes observed. For instance, we observed changes at the main stress responders rpoS and rpoE that, in combination with alterations at specific genes such as fliA, can explain the enhanced motility in the E. coli ?ihf strain. Altogether, in this thesis, we provided evidence that CRP, IHF and Fis control the activity of the promoter regions of genes involved in the planktonic-biofilm development. / Na natureza, o biofilme é uma estrutura complexa resultante de comunidades bacterianas multicelulares que fornece importantes funções nutricionais e a aquisição de traços de proteção como resistência a antibióticos e transferência horizontal de genes. O desenvolvimento das bactérias planctônicas solitárias para uma estrutura de biofilme maduro consiste em três fases principais: motilidade, fixação e maturação do biofilme. Ao nível celular, o processo é controlado por vários genes tais como flhD, fliA, rpoS, csgD, adrA, cpxR, todos agindo como reguladores mestre. Além disso, os reguladores globais CRP, IHF, Fis e outros em menor freqüência, têm sido relacionados à formação de biofilme, embora tenham sido fornecidas informações nao conclusivas sobre esse processo. Nesta tese foram utilizadas abordagens de bioinformática, assim como de biologia molecular e celular para entender o efeito de CRP, IHF e Fis na rede reguladora da transição de motilidade para biofilme na bactéria Escherichia coli. No primeiro capítulo, utilizamos a análise de rede para reconstruir e analisar parte da rede regulatória descrita para modular o programa flagelo-biofilme. Com esta análise identificamos algumas interações críticas responsáveis pela transição planctônica-biofilme. Em seguida, selecionamos os dez principais nós efetores da rede e clonamos a região promotora desses genes em um sistema repórter. Conforme explicado amplamente no capítulo II, este sistema nos permitiu validar e sugerir novas interações na rede. Adicionalmente, a medição da atividade do promotor durante o desenvolvimento bacteriano mostra que a CRP, a IHF e a Fis modulam diferencialmente a maioria dos genes analisados sugerindo que estes Reguladores Globais participam para modular a expressão génica em diferentes fases do desenvolvimento de estado planctónico para biofilme. No capítulo três, para obter uma melhor visão geral de todo o processo, realizamos ensaios de motilidade, aderência / biofilme precoce e biofilmes maduros. Descrevemos a capacidade intrínseca de E. coli para realizar motilidade, adesão e biofilme maduro a 37 °C. Em contraste, a ausência de ihf, fis, bem como o fenômeno de Repressão de Catabolite de Carbono (CCR), levam a fenótipos alterados, tanto na motilidade como no desenvolvimento do biofilme. No final, discutimos como as mudanças da atividade do promotor de genes alvo, juntamente com a nossa análise de rede, poderia !xi explicar parte dos fenótipos alterados observados. Por exemplo, observamos mudanças nos principais respondedores de estresse rpoS e rpoE que, em combinação com alterações em genes específicos como fliA, podem explicar a motilidade aumentada na estirpe de E. coli ?ihf. Em conjunto, nesta tese, apresentamos evidências de que CRP, IHF e Fis controlam a atividade das regiões promotoras de genes envolvidos no desenvolvimento planctônico-biofilme.

Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput measurement devices are available, such as microarrays and sequencing techniques, regulatory networks have been intensively studied over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing regression models and apply them to construct regulatory networks which span trancription factors and microRNAs. We also propose an extended algorithm to address the local optimum issue in finding the Maximum A Posterjorj estimator. An E. coli mRNA expression microarray data set with known bona fide interactions is used to evaluate our models and we show that our regression networks with a properly chosen prior can perform comparably to the state-of-the-art regulatory network construction algorithm. Finally, we apply our models on a p53-related data set, NCI-60 data. By further incorporating available prior structural information from sequencing data, we identify several significantly enriched interactions with cell proliferation function. In both of the two data sets, we select specific examples to show that many regulatory interactions can be confirmed by previous studies or functional enrichment analysis. Through comparing statistical models, we conclude from the project that combining different models with over-representation analysis and prior structural information can improve the quality of prediction and facilitate biological interpretation. Keywords: regulatory network, variable selection, penalized maximum likelihood estimation, optimization, functional enrichment analysis. / Science, Faculty of / Graduate

A primary aim of Synthetic Biology is the design and implementation of biological systems that perform engineered functions. However, the assembly of double-stranded DNA molecules is a major barrier to this progress, as it remains time consuming and laborious. Here I present three improved methods for DNA assembly. The first is based on, and makes use of, BioBricks. The second method relies on overlap-extension PCR to assemble non-standard parts. The third method improves upon overlap extension PCR by reducing the number of steps and the time it takes to assemble DNA. Finally, I show how the PCR-based assembly methods presented here can be used, in concert, with in vivo homologous recombination in yeast to assemble as many as 19 individual DNA parts in one step. These methods will also be used to assemble an incoherent feedforward loop, gene regulatory network.