Recent developments in automation and novel experimental techniques have led to the accumulation of vast amounts of biological data and the emergence of numerous databases to store the wealth of information. Consequentially, bioinformatics have drawn considerable attention, accompanied by the development of a plethora of tools for the analysis of biological data. DNA microarrays constitute a prominent example of a high-throughput experimental technique that has required substantial contribution of bioinformatics tools. Following its popularity there is an on-going effort to integrate gene expression with other types of data in a common analytical approach. Pathway based microarray analysis seeks to facilitate microarray data in conjunction with biochemical pathway data and look for a coordinated change in the expression of genes constituting a pathway. However, it has been observed that genes in a pathway may show variable expression, with some appearing activated while others repressed. This thesis aims to add some contribution to pathway based microarray analysis and assist the interpretation of such observations, based on the fact that in all organisms a substantial number of genes take part in more than one biochemical pathway. It explores the hypothesis that the expression of such genes represents a net effect of their contribution to all their constituent pathways, applying statistical and data mining approaches. A heuristic search methodology is proposed to manipulate the pathway contribution of genes to follow underlying trends and interpret microarray results centred on pathway behaviour. The methodology is further refined to account for distinct genes encoding enzymes that catalyse the same reaction, and applied to modules, shorter chains of reactions forming sub-networks within pathways. Results based on various datasets are discussed, showing that the methodology is promising and may assist a biologist to decipher the biochemical state of an organism, in experiments where pathways exhibit variable expression.

Skin cancer is one of the most common forms of adult solid tumour. The incidence is increasing rapidly making skin cancer a major health problem in several countries. Cutaneous Malignant Melanoma (CMM) is the least common but the most life threatening type of skin cancers and is responsible for 90% of all skin malignancy associated deaths. The precise cellular and molecular etiology of malignant melanoma is quite complex and the molecular events directly related to melanoma progression are yet to be elucidated. However, recent advances in molecular biology have resulted in a clearer understanding of the cellular and molecular events of skin cancer development. The best-characterized locus associated with CMM development is the CDKN2A that maps to chromosome 9p21 and encodes for the cell cycle regulator p16 tumour suppressor gene (TSG), and is frequently inactivated in melanoma tumours. In addition to p16, other loci located in 9p21 appear to be important in CMM development and functional evidence for the presence of TSG(s) has been provided (Parris et al., 1999). The aim of our study is to contribute to the understanding of CMM development by isolating and characterising novel TSG(s) at this location. In order to pursue identifying potential TSG(s), we have developed several monochromosome hybrids using microcell mediated chromosome transfer, and evaluated the tumourigenicity of the constructed hybrids by anchorage independent growth in soft agar. For the molecular biology aspects, expression analysis of the genes in the 9p21 region was carried out by reverse transcription PCR. Potential candidate tumour suppressor genes were then carefully evaluated by generating expression profiles via conducting real time PCR. Experimental evidence is provided which supports the candidacy of interferon alpha 1 (IFNA1) as a tumour suppressor gene for melanoma development.

Various types of recurrent neural networks have been used as models for the regulatory relationships between genes. The neural network is trained on the data from micro-array techniques, each gene corresponds to a neuron in the network. The data from the micro-array technologies has numerous genes, but usually involves few samples, this makes the network heavily under-determined. In this work we will propose a method that can cope with the poorness of the data. We will use a Hopfield-type neural network to model the ontogenetic differentiation of female honeybees. A method that identifies the genes that determine the castes is proposed.

<p>Various types of recurrent neural networks have been used as models for the regulatory relationships between genes. The neural network is trained on the data from micro-array techniques, each gene corresponds to a neuron in the network. The data from the micro-array technologies has numerous genes, but usually involves few samples, this makes the network heavily under-determined. In this work we will propose a method that can cope with the poorness of the data. We will use a Hopfield-type neural network to model the ontogenetic differentiation of female honeybees. A method that identifies the genes that determine the castes is proposed.</p>

<p>In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.</p>

Serine/Arginine-rich proteins (SR-proteins) have been well characterized in metazoans and in the flowering plant Arabidopsis thaliana. But so far no attempts on characterizing SR-proteins in the moss Physcomitrella patens have been done. SR-proteins are a conserved family of splicing regulators essential for constitutive- and alternative splicing. SR-proteins are mediators of alternative splicing (AS) and may be alternatively spliced themselves as a form of gene regulation. Three novel SR-proteins of the SR-subfamily were identified in P. patens. The three genes show conserved intron-exon structure and protein domain distribution, not surprising since the gene family has evidently evolved through gene duplications. The SR-proteins PpSR40 and PpSR36 show differential tissue-specific expression, whereas PpSR39 does not. Tissue-specific expression of SR-proteins has also been seen in A. thaliana. SR-proteins determine splice-site usage in a concentration dependent manner. SR-protein overexpression experiments in A. thaliana and Oryza sativa have shown alteration of splicing patterns of endogenous SR-proteins. Overexpression of PpSR40 did not alter the splicing patterns of PpSR40, PpSR36 and PpSR39. This suggests that they might not be a substrate for PpSR40. These first results of SR-protein characterization in P. patens may provide insights on the SR-protein regulation mechanisms of the common land plant ancestor.

Aging, characterized by a time-dependent functional decline, eventually results in the death of an organism. Unfortunately, this complex biological phenomenon is poorly understood. In order to dissect the molecular changes associated with aging, the identification and molecular characterization of the genes that regulate this universal process is absolutely necessary. The expectation is that the isolated genes potentially have human homologues and can be experimentally analyzed in Drosophila melanogaster in order to determine basic function. In an attempt to find candidate genes that may influence aging, the enhancer trap technique was utilized to identify age-related regulatory elements. The genomic regions surrounding the insertion site of the enhancer trap lines have the potential to be regulated by the characterized enhancer. A previous screen determined the temporal pattern of 180 enhancers trap lines, known as DJ lines. Many of these lines demonstrated an expression pattern that was associated with age. Several of the genes within the nearby genomic regions of six sequenced DJ lines, DJ695, DJ710, DJ849, DJ767, DJ761 and DJ694, were chosen for transcript quantification. Prior to gene quantification, reverse transcription, an essential step in the experimental procedure, was assessed for the error it incorporated into quantification. Specifically, an exogenous molecule was used to ensure that unsuccessful reverse transcription reactions had the potential to be identified and, soon after, discarded. This was achieved through the use of a spike RNA molecule, Luciferase. Luciferase was shown to be a diagnostic tool that can be used in determining reverse transcription efficiency. Eight genes were chosen from the aforementioned DJ lines and quantitative PCR revealed that the natural regulation of some genes were comparable to the, previously obtained, expression pattern of the enhancer trap line. Although the expression of other genes did not correlate to that of the enhancer trap lines, all genes exhibited expression patterns that were age-associated. The known functions of these candidate genes and the relevant homologues are discussed. These findings validate the use of the enhancer trap technique in the identification of candidate genes involved in the aging process. / Thesis (Master, Biology) -- Queen's University, 2009-02-09 10:59:16.871

No description available.

The Breast cancer gene 1 (BRCA1) codes for nuclear phosphoprotein with a key function in the regulation of DNA damage response. The BRCA1 protein contributes to the formation and regulation of protein supercomplexes that participates on the DNA double-strand break repair. These protein supercomplexes are formed by the protein-protein interactions between highly conservative protein motives in BRCA1 and its binding partners. Except to the wild type form of BRCA1 mRNA containing entire set of 22 exons coding for the 220 kD protein, numerous alternative splicing variants (ASVs) BRCA1 mRNA has been described. These ASVs code for BRCA1 isoforms lacking several critical functional domains. It has been proposed, that formation of BRCA1's ASVs represent a tool for regulation of BRCA1 function. Only poorly has been characterized a complex catalogue of in various human tissues and their expression. This study aims to address these questions. We optimized the identification of BRCA1's ASVs including those covering the entire transcripts of the wt BRCA1 mRNA with length exceeding 5.5 kb. In further analysis, we characterized 13 BRCA1's ASVs in RNA samples isolated from peripheral blood mononuclear cells (PBMNC) obtained from patients with breast cancer (BC) and control subjects. The majority of the identified...

Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be included a priori as mandatory covariates while allowing the selection of a large number of candidate or optional variables. As genomic studies routinely require mandatory covariates, it is of interest to propose principled methods of variable selection that can incorporate mandatory covariates. Methods: In this article, we propose the ridge-lasso hybrid estimator (ridle), a new penalized regression method that simultaneously estimates coefficients of mandatory covariates while allowing selection for others. The ridle provides a principled approach to mitigate effects of multicollinearity among the mandatory covariates and possible dependency between mandatory and optional variables. We provide detailed empirical and theoretical studies to evaluate our method. In addition, we develop an efficient algorithm for the ridle. Software, based on efficient Fortran code with R-language wrappers, is publicly and freely available at https://sites.google.com/site/zhongyindaye/software. Results: The ridle is useful when mandatory predictors are known to be significant due to prior knowledge or must be kept for additional analysis. Both theoretical and comprehensive simulation studies have shown that the ridle to be advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves. A microarray gene expression analysis of the histologic grades of breast cancer has identified 24 genes, in which 2 genes are selected only by the ridle among current methods and found to be associated with tumor grade. Conclusions: In this article, we proposed the ridle as a principled sparse regression method for the selection of optional variables while incorporating mandatory ones. Results suggest that the ridle is advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves.