The temporal and spatial patterning that underlies morphogenetic events is controlled by gene regulatory networks (GRNs). These operate through a combinatorial code of DNA – binding transcription factor proteins, and non – coding DNA sequences (cis-regulatory elements, or CREs), that specifically bind transcription factors and regulate nearby genes. By comparatively studying the development of different species, we can illuminate lineage – specific changes in gene regulation that account for morphological evolution. The central nervous system of vertebrates is composed of diverse neural cells that undergo highly coordinated programs of specialization, migration and differentiation during development. Approximately 20% of neurons in the cerebral cortex are GABAergic inhibitory interneurons, which release the neurotransmitter gamma-aminobutyric acid (GABA). Diseases such as autism, schizophrenia and epilepsy are associated with defects in GABAergic interneuron function. Several members of the distal-less homeobox (Dlx) transcription factor family are implicated in a GRN underlying early GABAergic interneuron development in the forebrain. I examined the role played by orthologous dlx genes in the development of GABAergic interneurons in the zebrafish forebrain. I found that when ascl1a transcription factor is down-regulated through the micro-injection of translation – blocking morpholino oligonucleotides, Dlx gene transcription is decreased in the diencephalon, but not the telencephalon. Similarly, gad1a transcription is also decreased in this region for these morphants. As gad1a encodes an enzyme necessary for the production of GABA, these genes are implicated in a cascade underlying GABAergic interneuron development in the diencephalon.

Computational gene regulation models provide a means for scientists to draw biological inferences from large-scale gene expression data. The expression data used in the models usually are obtained in a time series in response to an initial perturbation. The common objective is to reverse engineer the internal structure and function of the genetic network from observing and analyzing its output in a time-based fashion. In many studies (Wang [39], Resendis-Antonio [31]), each gene is considered to have a regulatory effect on another gene. A network association is created based on the correlation of expression data. Highly correlated genes are thought to be co-regulated by similar (if not the same) mechanism. Gene co-regulation network models disregard the cascading effects of regulatory genes such as transcription factors, which could be missing in the expression data or are expressed at very low concentrations and thus undetectable by the instrument. As an alternative to the former methods, some authors (Wu et al. [40], Rangel et al. [28], Li et al. [20]) have proposed treating expression data solely as observation values of a state-space system and derive conceptual internal regulatory elements, i.e. the state-variables, from these measurements. This approach allows one to model unknown biological factors as hidden variables and therefore can potentially reveal more complex regulatory relations.<p>In a preliminary portion of this work, two state-space models developed by Rangel et al. and Wu et al. respectively were compared. The Rangel model provides a means for constructing a statistically reliable regulatory network. The model is demonstrated on highly replicated Tcell activation data [28]. On the other hand, Wu et al. develop a time-delay module that takes transcriptional delay dynamics into consideration. The model is demonstrated on non-replicated yeast cell-cycle data [40]. Both models presume time-invariant expression data. Our attempt to use the Wu model to infer small gene regulatory network in yeast was not successful. Thus we develop a new modeling tool incorporating a time-lag module and a novel method for constructing regulatory networks from non-replicated data. The latter involves an alternative scheme for determining network connectivity. Finally, we evaluate the networks generated from the original and extended models based on a priori biological knowledge.

This paper will consider how regulatory arbitrage contributed to the 2007-2009 financial crisis (the “financial crisis”). In particular, the paper will establish how the avoidance of regulatory capital requirements by large and complex financial institutions (“LC financial institutions”) severely worsened the financial crisis, necessitating a massive rent extraction from U.S. taxpayers. In doing to, the paper will examine the regulatory arbitrage perpetrated by American International Group and the subsequent U.S. taxpayer bailout of that firm. Because of the enormous amount of sovereign credit that had to be substituted for private capital during the financial crisis the paper assumes that the net negative nature of regulatory avoidance by LC financial institutions is axiomatic. Therefore, the paper advances several possible reform measures that could eventually be implemented into a new legal framework to confront the problems that are posed by the avoidance of financial services regulations.

This paper will consider how regulatory arbitrage contributed to the 2007-2009 financial crisis (the “financial crisis”). In particular, the paper will establish how the avoidance of regulatory capital requirements by large and complex financial institutions (“LC financial institutions”) severely worsened the financial crisis, necessitating a massive rent extraction from U.S. taxpayers. In doing to, the paper will examine the regulatory arbitrage perpetrated by American International Group and the subsequent U.S. taxpayer bailout of that firm. Because of the enormous amount of sovereign credit that had to be substituted for private capital during the financial crisis the paper assumes that the net negative nature of regulatory avoidance by LC financial institutions is axiomatic. Therefore, the paper advances several possible reform measures that could eventually be implemented into a new legal framework to confront the problems that are posed by the avoidance of financial services regulations.

Identification of the mechanisms by which genes are regulated in eukaryotes is one of the principal challenges of modern biology. The emergence of genome sequencing has facilitated the marked expansion of experimental and computational approaches designed to address this challenge. Integrating and assessing this information remains a major scientific endeavor that requires new and innovative application of technology. Furthermore, our limited understanding of the mechanisms of gene regulation in eukaryotes has undermined our ability to understand the role of genetics in gene regulation. Regulatory variants are thought to be responsible for a considerable amount of the heterogeneity within our population and to be fundamental determinants of health. New experimental approaches offer the opportunity to effectively identify markers of disease susceptibility in gene regulatory regions but the discovery of the molecular mechanism of dysregulation remains difficult and time-consuming. It is here where computational approaches are required to prioritize candidate regulatory variants. To do so requires the development of an extensive control set from which characteristic signals can be identified. This thesis introduces novel approaches for discovering, utilizing, comparing and visualizing regulatory element predictions in completed genomes. This thesis also introduces novel bioinformatics infrastructure for curating regulatory element and variant datasets, and introduces the largest-available, open-access dataset of functional regulatory variants hand-curated from literature. This dataset is used to identify signals which discriminate functional variants from other variants in the promoter regions of human genes using regulatory and population genetics-based computational approaches.

Identification of the mechanisms by which genes are regulated in eukaryotes is one of the principal challenges of modern biology. The emergence of genome sequencing has facilitated the marked expansion of experimental and computational approaches designed to address this challenge. Integrating and assessing this information remains a major scientific endeavor that requires new and innovative application of technology. Furthermore, our limited understanding of the mechanisms of gene regulation in eukaryotes has undermined our ability to understand the role of genetics in gene regulation. Regulatory variants are thought to be responsible for a considerable amount of the heterogeneity within our population and to be fundamental determinants of health. New experimental approaches offer the opportunity to effectively identify markers of disease susceptibility in gene regulatory regions but the discovery of the molecular mechanism of dysregulation remains difficult and time-consuming. It is here where computational approaches are required to prioritize candidate regulatory variants. To do so requires the development of an extensive control set from which characteristic signals can be identified. This thesis introduces novel approaches for discovering, utilizing, comparing and visualizing regulatory element predictions in completed genomes. This thesis also introduces novel bioinformatics infrastructure for curating regulatory element and variant datasets, and introduces the largest-available, open-access dataset of functional regulatory variants hand-curated from literature. This dataset is used to identify signals which discriminate functional variants from other variants in the promoter regions of human genes using regulatory and population genetics-based computational approaches.

Computational gene regulation models provide a means for scientists to draw biological inferences from large-scale gene expression data. The expression data used in the models usually are obtained in a time series in response to an initial perturbation. The common objective is to reverse engineer the internal structure and function of the genetic network from observing and analyzing its output in a time-based fashion. In many studies (Wang [39], Resendis-Antonio [31]), each gene is considered to have a regulatory effect on another gene. A network association is created based on the correlation of expression data. Highly correlated genes are thought to be co-regulated by similar (if not the same) mechanism. Gene co-regulation network models disregard the cascading effects of regulatory genes such as transcription factors, which could be missing in the expression data or are expressed at very low concentrations and thus undetectable by the instrument. As an alternative to the former methods, some authors (Wu et al. [40], Rangel et al. [28], Li et al. [20]) have proposed treating expression data solely as observation values of a state-space system and derive conceptual internal regulatory elements, i.e. the state-variables, from these measurements. This approach allows one to model unknown biological factors as hidden variables and therefore can potentially reveal more complex regulatory relations.<p>In a preliminary portion of this work, two state-space models developed by Rangel et al. and Wu et al. respectively were compared. The Rangel model provides a means for constructing a statistically reliable regulatory network. The model is demonstrated on highly replicated Tcell activation data [28]. On the other hand, Wu et al. develop a time-delay module that takes transcriptional delay dynamics into consideration. The model is demonstrated on non-replicated yeast cell-cycle data [40]. Both models presume time-invariant expression data. Our attempt to use the Wu model to infer small gene regulatory network in yeast was not successful. Thus we develop a new modeling tool incorporating a time-lag module and a novel method for constructing regulatory networks from non-replicated data. The latter involves an alternative scheme for determining network connectivity. Finally, we evaluate the networks generated from the original and extended models based on a priori biological knowledge.

The People’s Bank of China (PBC) has played a vital role in conducting monetary policy during a period of fast economic growth in China. The PBC used a variety of monetary measures and instruments to implement monetary policy over the past decades. In this project, we examined whether the implementation of monetary policy in China mimics the market economy or not. We summarise the main tools that have been used in the monetary policy based on the studies by Qiong (2011) and Gerlach (2004). We then applied the variance and covariance analysis of macroeconomic variables of China and the US for two periods. The results of our analysis showed that China and the US had many similarities. Our empirical analysis, using the Taylor Rule theoretical framework demonstrates that the difference between the actual interest rate and the theoretical interest rate derived from the Taylor Rule for China and the US has the same trend. Our main conclusion is that the implementation of monetary policy in China mimics the market economy.

<p>Higgins' regulatory focus theory (1997) postulates two cognitive/motivational systems for pursuing desired end states: the promotion and prevention systems. The theory predicts that failure in each system is discriminantly associated with dysphoric and anxious affect respectively; and that significant failure in these systems creates vulnerability to depression and anxiety. This study tested these hypotheses among adolescents who took part in the longitudinal Wisconsin Study of Families and Work. We found partial support for the theory's predictions. Specifically, the original adult Selves Questionnaire (SQ), which was administered at age 13, did not demonstrate the expected discriminant associations with dysphoric and anxious affect and symptoms. However, the Selves Questionnaire - Adolescent Version, which was administered at age 15, yielded partial support for the theory. Ideal self-discrepancy was discriminantly associated with depressive affect but ought self-discrepancy was not discriminantly associated with anxious affect. However, feared self-discrepancy was discriminantly associated with anxious affect, which adds to the literature suggesting that feared self-discrepancy might be a better construct to use in measuring prevention failure among adolescents. The association between self-discrepancy and affect was found cross-sectionally but not longitudinally. The study also tested recently formulated predictions of regulatory focus theory which state that significant failure in one regulatory system is likely to negatively impact the other system (Klenk, Strauman, & Higgins, 2011). No support for this prediction was found. Implications of the findings, and aspects of the study that may have reduced our ability to test the hypotheses of interest, are discussed.</p> / Dissertation

Two models are proposed to describe interactions among genes, transcription factors, and signaling cascades involved in regulating a cellular sub-system. These models fall within the class of Markovian regulatory networks, and can accommodate for different biological time scales. These regulatory networks are used to study pathological cellular dynamics and discover treatments that beneficially alter those dynamics. The salient translational goal is to design effective therapeutic actions that desirably modify a pathological cellular behavior via external treatments that vary the expressions of targeted genes. The objective of therapeutic actions is to reduce the likelihood of the pathological phenotypes related to a disease. The task of finding effective treatments is formulated as sequential decision making processes that discriminate the gene-expression profiles with high pathological competence versus those with low pathological competence. Thereby, the proposed computational frameworks provide tools that facilitate the discovery of effective drug targets and the design of potent therapeutic actions on them. Each of the proposed system-based therapeutic methods in this dissertation is motivated by practical and analytical considerations. First, it is determined how asynchronous regulatory models can be used as a tool to search for effective therapeutic interventions. Then, a constrained intervention method is introduced to incorporate the side-effects of treatments while searching for a sequence of potent therapeutic actions. Lastly, to bypass the impediment of model inference and to mitigate the numerical challenges of exhaustive search algorithms, a heuristic method is proposed for designing system-based therapies. The presentation of the key ideas in method is facilitated with the help of several case studies.