The long-term success of transplantation is limited by the need for immunosuppression; thus, tolerance induction is an important therapeutic goal. A 16-day treatment with rapamycin in mice led to indefinite graft survival of fully mismatched cardiac allografts, whereas untreated hearts were rejected after 8-10 days. Specific tolerance was confirmed through subsequent skin grafts and in vitro lymphocyte assays that showed recipient mice remained immunocompetent towards 3rd party antigens but were impaired in responding to donor antigens. Mechanisms that account for this tolerant state were then investigated. Splenic CD8+CD44+ memory T-cells were reduced in tolerant mice but had increased frequencies of the CD62LLO population. CD4+CD25+Foxp3+ regulatory T-cells were increased in tolerant mice. Through multiplex PCR, 4 regulatory T-cell related genes were found up-regulated and 2 proinflammatory genes were down-regulated in accepted hearts. This expression pattern may serve as a putative biomarker of tolerance in patients undergoing transplantation.

Banks within Europe have a major role in the European financial system. The financial collapse in 2008 made regulators well aware of the importance of corporate transparency to allow stakeholders to assess the banks health and maintain a stable market. Risk reporting within the European Union (EU) contributes to transparency in terms of disclosing information on risk management activities. The heavy regulations and demand from investors have caused the extent of risk reports to increase over time. The purpose of this research is to investigate if there is a relationship between the quantity of information in risk disclosures and the financial performance for banks in the EU and thus contribute with new knowledge to the field of finance, and increase managers' as well as stakeholders' understanding of the impact of risk reports. The methodological standpoints guide our choices throughout the research process. Our epistemological view is positivism and our ontological view is objectivism. A deductive research approach and a quantitative research method are adopted to collect archival data from risk reports and on financial performance from a sample of 41 banks. Our population consist of banks within the EU. The research design is cross-sectional using data from one point in time, the time period 2013-04-01 - 2014-03-31. Based on relevant theories and previous research, quantity proxies in terms of number of pages, words, characters and recurrence of keywords together with financial performance measures in terms of stock return, standard deviation and beta are used to investigate the relationship. 3 hypotheses are derived and tested by running regressions where the financial performance measures are the dependent variables and our proxies for quality are the independent variables. Our tests show that no significant relationship exists between the quantity of information in risk disclosures and the financial performance of banks within the EU. The results from our research contribute with new knowledge to academics within the field of finance by increasing the understanding of the explanatory variables for financial performance. Moreover, academics may use our results to justify the choice of other proxies than quantity when investigating quality in corporate disclosures. Additionally, our results indicate that practitioners should not use quantity of information in risk reports as an indicator of quality, as no relationship with the financial performance of a bank could be statistically proven.

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.

Interferon regulatory factor 3 (IRF3) plays an important role in activating the innate immune response in a variety of conditions, including viral infection. As well as regulating the immune response to viruses, IRF3 is involved in regulating cellular functions including apoptosis. Apoptosis and the inflammatory response to viral infection are very different; therefore, it is obvious that IRF3 plays dramatically different roles in the cell depending on the conditions. We previously identified a non-activating phosphorylation of IRF3 in response to adenovirus (Ad) in which Serine-173 is phosphorylated. In addition to Ad infection, IRF3- S173 is phosphorylated in response to genotoxic stresses including ultraviolet (UV) irradiation and etoposide. In this study, I show that this phosphorylation event is involved in a variety of processes including protein stability, cell survival and IRF3 regulation. Thus, phosphorylation of IRF3-S173 is a novel and important event in a complex regulatory pathway of an integral protein.

I investigate aspects of fleet dynamics in a mobile gear, groundfish fishery, on the Scotian Shelf; an area subject to a seasonal area closure. Firstly, the direct impacts of the closure on the redistribution of fishing effort and the resultant catch rates of those “fishing the line” (FTL) were examined. Effort was found to concentrate within 30km of the closure boundary. Two areas of potential FTL strategy were identified, which produced variable catch rate trends. East of the closure, areas of highest catch rate corresponded to areas of greatest effort, while to the west, catch rate was often equalized throughout the region, analogous to the ideal free distribution (IFD). Secondly, two effort distributional models were compared: an IFD-based isodar model and a discrete choice model. The isodar was determined to be the preferred model because of both its consistently superior predictive performance and its greater simplicity.

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

Evolutionary process can be inferred at three different levels: the species level, the population level and the molecular level. In this thesis, I applied approaches at these three levels and aimed to get a comprehensive picture of conifer evolution, from speciation and demography to geographic variation and local adaptation, and then to the molecular evolution of proteins and small regulatory RNAs. Spruce species have been observed to possess a large number of trans-species shared polymorphisms. Using an “Isolation with migration” model, we found that the large effective population size of spruce retained these shared polymorphisms, inheriting them from the common ancestor. Post-divergence gene flow only existed between Picea abies and P. glauca, and between P. wilsonii and P. schrenkiana. The combination of Tajima’s D and Fay & Wu’s H at most of loci suggested an ancient and severe bottleneck for most species except P. breweriana. Furthermore, I investigated the effect of local selection in two parallel clines, which is one of the major forces that can cause divergence or even speciation. The timing of bud set and growth cessation was found correlated with latitude in populations of P. abies and P. obovata. Using allele frequency spectrum analyses we identified three genes under local selection in both species including two circadian-clock genes GI and PRR7, and one photoperiodic gene FTL2. This indicated that parallel evolution could occur through groups of genes within related pathways. Clinal variation at expression level provided stronger evidence of selection in FTL2, which has previously been associated with bud set in P. abies. Finally we focused on the molecular evolution of mRNA and small regulatory RNAs in P. abies. With the help of Next-Generation sequencing, we have achieved in spruce the first de novel assembly of the needle transcriptome and a preliminary characterization of sRNA populations. Along with features common in plants, spruce also exhibited novelties in many aspects including lower substitution rate and protein evolutionary rate, dominance of 21-nt sRNA, and a large proportion of TIR-NBS-LRR genes as sRNA sources and targets.

The core focus of this research lies in developing and using intelligent methods to solve biological problems and integrating the knowledge for understanding the complex gene regulatory phenomenon. We have developed an integrative framework and used it to: model molecular interactions from separate case studies on time-series gene expression microarray datasets, molecular sequences and structure data including the functional role of microRNAs; to extract knowledge; and to build reusable models for the central dogma theme. Knowledge was integrated with the use of ontology and it can be reused to facilitate new discoveries as demonstrated on one of our systems – the Brain Gene Ontology (BGO). The central dogma theme states that proteins are produced from the DNA (gene) via an intermediate transcript called RNA. Later these proteins play the role of enzymes to perform the checkpoints as a gene expression control. Also, according to the recently emerged paradigm, sometimes genes do not code for proteins but results in small molecules of microRNAs which in turn controls the gene regulation. The idea is that such a very complicated molecular biology process (central dogma) results in production of a wide variety of data that can be used by computer scientists for modelling and to enable discoveries. We have suggested that this range of data should actually be taken into account for analysis to understand the concept of gene regulation instead of just taking one source of data and applying some standard methods to reveal facts in the system biology. The problem is very complex and, currently, computational algorithms have not been really successful because either existing methods have certain problems or the proven results were obtained for only one domain of the central dogma of molecular biology, so there has always been a lack of knowledge integration. Proper maintenance of diverse sources of data, structures and, in particular, their adaptation to new knowledge is one of the most challenging problems and one of the crucial tasks towards the knowledge integration vision is the efficient encoding of human knowledge in ontologies. More specifically this work has contributed towards the development of novel computational and information science methods and we have promoted the vision of knowledge integration by developing brain gene ontology (BGO) system. With the integrative use of several bioinformatics methods, this research has indeed resulted in modelling of such knowledge that has not been revealed in system biology so far. There are many discoveries made during my study and some of the findings are briefly mentioned as follows: (1) in relation to leukaemia disease we have discovered a new gene “TCF-1” that interacts with the “telomerase” gene. (2) With respect to yeast cell cycle analysis, we hypothesize that exoglucanase gene “exg1” is now implicated to be tied with “MCB cluster regulation” and a “mannosidase” with “histone linked mannoses”. A new quantitative prediction is that the time delay of the interaction between two genes seems to be approximately 30 minutes, or 0.17 cell cycles. Next, Cdc22, Suc22 and Mrc1 genes were discovered that interacts with each other as the potential candidates in controlling the Ribonucleotide reductase (RNR) activity. (3) Upon studying the phenomenon of Long Term Potentiation (LTP) it was found that the transcription factors, responsible for regulation of gene expression, begin to be elevated as soon as 30 min after induction of LTP, and remain elevated up to 2 hours. (4) Human microRNA data investigation resulted in the successful identification of two miRNA families i.e. let-7 and mir-30. (5) When we analysed the CNS cancer data, a set of 10 genes (HMG-I(Y), NBL1, UBPY, Dynein, APC, TARBP2, hPGT, LTC4S, NTRK3, and Gps2) was found to give 85% correct prediction on drug response. (6) Upon studying the AMPA, GABRA and NMDA receptors we hypothesize that phenylalanine (F at position 269) and leucine (L at position 353) in these receptors play the role of a binding centre for their interaction with several other genes/proteins such as c-jun, mGluR3, Jerky, BDNF, FGF-2, IGF-1, GALR1, NOS and S100beta. All the developed methods that we have used to discover above mentioned findings are very generic and can be easily applied on any dataset with some constraints. We believe that this research has established the significant fact that integrative use of various computational intelligence methods is critical to reveal new aspects of the problem and finally knowledge integration is also a must. During this coursework, I have significantly published this research in reputed international journals, presented results in several conferences and also produced book chapters.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 117-142).