In recent time particular interest has been drawn to the inference of gene regulatory networks from microarray gene expression data. But despite major improvements with data based methods, the network reconstruction from expression data alone still presents a computationally complex (NP-hard) problem. In this work it is incorporated additional information – regulatory motifs from yeast, when inferring a gene regulatory network for mouse genes. It was put forward the hypothesis that regulatory patterns analogous to these motifs are present in the set of mouse genes and can be identified by comparing yeast and mouse genes in terms of sequence similarity or Gene Ontology (The Gene Ontology Consortium 2000) annotations. In order to examine this hypothesis, small permutations of genes with high similarity to such yeast gene regulatory motifs were first tested against simple data-driven regulatory networks by means of consistency with the expression data. And secondly, using the best scored interactions provided by these permutations it were then inferred networks for the whole set of mouse genes. The results showed that individual permutations of genes with a high similarity to a given yeast motif did not perform better than low scored motifs and that complete networks, which were inferred from regulatory interactions provided by permutations, did also neither show any noticeable improvement over the corresponding data-driven network nor a high consistency with the expression data at all. It was therefore found that the hypothesis failed, i.e. neither the use of sequence similarity nor searching for identical functional annotations between mouse and yeast genes allowed to identify sets of genes that showed a high consistency with the expression data or would have allowed for an improved gene regulatory network inference.

<p>In recent time particular interest has been drawn to the inference of gene regulatory networks from microarray gene expression data. But despite major improvements with data based methods, the network reconstruction from expression data alone still presents a computationally complex (NP-hard) problem. In this work it is incorporated additional information – regulatory motifs from yeast, when inferring a gene regulatory network for mouse genes. It was put forward the hypothesis that regulatory patterns analogous to these motifs are present in the set of mouse genes and can be identified by comparing yeast and mouse genes in terms of sequence similarity or Gene Ontology (The Gene Ontology Consortium 2000) annotations.</p><p>In order to examine this hypothesis, small permutations of genes with high similarity to such yeast gene regulatory motifs were first tested against simple data-driven regulatory networks by means of consistency with the expression data. And secondly, using the best scored interactions provided by these permutations it were then inferred networks for the whole set of mouse genes.</p><p>The results showed that individual permutations of genes with a high similarity to a given yeast motif did not perform better than low scored motifs and that complete networks, which were inferred from regulatory interactions provided by permutations, did also neither show any noticeable improvement over the corresponding data-driven network nor a high consistency with the expression data at all.</p><p>It was therefore found that the hypothesis failed, i.e. neither the use of sequence similarity nor searching for identical functional annotations between mouse and yeast genes allowed to identify sets of genes that showed a high consistency with the expression data or would have allowed for an improved gene regulatory network inference.</p>

Inferring regulatory relationships between genes, including the direction and the nature of influence between them, is the foremost problem in the field of genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. More specifically, a “surprising” situation occurs when the phenotype of a double mutant has a similar, aggravating or alleviating effect compared to the phenotype resulting from the single deletion of either one of the genes. As useful as this broad approach has been, there are limits to its ability to discriminate alternative pathway structures, meaning it is not always possible to infer the relationship between the genes. Here, we explore the possibility of dynamic epistasis analysis. In addition to performing genetic perturbations, we drive a genetic pathway with a dynamic, time-varying upstream signal, where the phenotypic consequence is measured at each time step. We explore the theoretical power of dynamic epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We also explore the identifiability of individual links in the pathway. Through these evaluations, we quantify how helpful the addition of dynamics is. We believe that a dynamic input in addition to epistasis analysis is a powerful tool to discriminate between different networks. Our primary findings show that the use of a dynamic input signal alone, without genetic perturbations, appears to be very weak in comparison with the more traditional genetic approaches based on the deletion of genes. However, the combination of dynamical input with genetic perturbations is far more powerful than the classical epistasis analysis approach. In all cases, we find that even relatively simple input dynamics with gene deletions greatly increases the power of epistasis analysis to discriminate alternative network structures and to confidently identify individual links in a network. Our positive results show the potential value of dynamics in epistasis analysis.

Hematopoietic systems in vertebrates have been the focus of intense study. However immunocyte development is well characterized in very few invertebrate groups. The sea urchin is an attractive model for the study of immune cell development. Larval immunocytes, pigment cells and derivatives of the blastocoelar cells, emerge from a small population of precursors specified at blastula stage. Analyses from the genome reveal a complex system of immune receptors and effectors and a near complete set of homologues of vertebrate transcriptional regulators. Characterization of the expression profile and function of sea urchin homologues of key vertebrate hematopoietic transcription factors imply a conserved role in immunocyte development. SpGatac, an orthologue of the vertebrate Gata-1/2/3 transcription factors and SpScl, an orthologue of Scl/Tal-2/Lyl-1 transcription factors are both required for immune cell specification in the embryo. An important cis-regulatory mechanism that restricts SpGatac expression to the blastocoelar cells involves repression by SpGcm in the pigment cells. Characterization of the expression of several additional transcription factors, including SpE2A, an orthologue of vertebrate E2A/HEB/ITF2, SpId, an orthologue of the Class V bHLH factors that modulate E-protein function, and SpLmo2, an orthologue of the cofactor part of the transcriptional complex that includes Scl and Gata family members, suggests the existence of a conserved regulatory complex for hematopoiesis. Two isoforms of the SpE2A gene were identified. The shorter isoform shares genomic organization and sequence conservation with the mouse paralogue of E2A, HEBAlt. Expression of SpE2A and SpE2AAlt is consistent with a function in immunocyte development in the sea urchin embryo. Findings of the counterpart to a key vertebrate regulatory system functioning in the development of immunocytes in the simple sea urchin embryo lay the foundation for comparative immunocyte developmental gene regulatory network analyses. These will in turn lead to a greater understanding of the evolution of immune systems across phyla and will provide simple invertebrate model systems for detailed comparative investigations of regulatory function with direct relevance to vertebrates.

Hematopoietic systems in vertebrates have been the focus of intense study. However immunocyte development is well characterized in very few invertebrate groups. The sea urchin is an attractive model for the study of immune cell development. Larval immunocytes, pigment cells and derivatives of the blastocoelar cells, emerge from a small population of precursors specified at blastula stage. Analyses from the genome reveal a complex system of immune receptors and effectors and a near complete set of homologues of vertebrate transcriptional regulators. Characterization of the expression profile and function of sea urchin homologues of key vertebrate hematopoietic transcription factors imply a conserved role in immunocyte development. SpGatac, an orthologue of the vertebrate Gata-1/2/3 transcription factors and SpScl, an orthologue of Scl/Tal-2/Lyl-1 transcription factors are both required for immune cell specification in the embryo. An important cis-regulatory mechanism that restricts SpGatac expression to the blastocoelar cells involves repression by SpGcm in the pigment cells. Characterization of the expression of several additional transcription factors, including SpE2A, an orthologue of vertebrate E2A/HEB/ITF2, SpId, an orthologue of the Class V bHLH factors that modulate E-protein function, and SpLmo2, an orthologue of the cofactor part of the transcriptional complex that includes Scl and Gata family members, suggests the existence of a conserved regulatory complex for hematopoiesis. Two isoforms of the SpE2A gene were identified. The shorter isoform shares genomic organization and sequence conservation with the mouse paralogue of E2A, HEBAlt. Expression of SpE2A and SpE2AAlt is consistent with a function in immunocyte development in the sea urchin embryo. Findings of the counterpart to a key vertebrate regulatory system functioning in the development of immunocytes in the simple sea urchin embryo lay the foundation for comparative immunocyte developmental gene regulatory network analyses. These will in turn lead to a greater understanding of the evolution of immune systems across phyla and will provide simple invertebrate model systems for detailed comparative investigations of regulatory function with direct relevance to vertebrates.

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.

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.