<p>Proteins combine in various ways to execute different essential functions. Cellular processes are enormously complex and it is a great challenge to explain the underlying organization. Various methods have been applied in attempt to reveal the organization of the cell. Gene expression analysis uses the mRNA levels in the cell to predict which proteins are present in the cell simultaneously. This method is useful but also known to sometimes fail. Proteins that are known to be functionally related do not always show a significant correlation in gene expression. This fact might be explained by the dynamic organization of the proteome. Proteins can have diverse functions and might interact with some proteins only during a few time points, which would probably not result in significant correlation in their gene expression. In this work we tried to address this problem by combining gene expression data with data for physical interactions between proteins. We used a method for modular decomposition introduced by Gagneur et al. (2004) that aims to reveal the logical organization in protein-protein networks. We extended the interpretation of the modular decomposition to localize the dynamics in the protein organization. We found evidence that protein-interactions supported by gene expression data are very likely to be related in function and thus can be used to predict function for unknown proteins. We also identified negative correlation in gene expression as an overlooked area. Several hypotheses were generated using combination of these methods. Some could be verified by the literature and others might shed light on new pathways after additional experimental testing.</p>

This thesis presents several developments on Variance component (VC) approach for Quantitative Trait Locus (QTL) mapping. The first part consists of methodological improvements: a new fast and efficient method for estimating IBD matrices, have been developed. The new method makes a better use of the computer resources in terms of computational power and storage memory, facilitating further improvements by resolving methodological bottlenecks in algorithms to scan multiple QTL. A new VC model have also been developed in order to consider and evaluate the correlation of the allelic effects within parental lines origin in experimental outbred crosses. The method was tested on simulated and experimental data and revealed a higher or similar power to detect QTL than linear regression based QTL mapping. The second part focused on the prospect to analyze multi-generational pedigrees by VC approach. The IBD estimation algorithm was extended to include haplotype information in addition to genotype and pedigree to improve the accuracy of the IBD estimates, and a new haplotyping algorithm was developed for limiting the risk of haplotyping errors in multigenerational pedigrees. Those newly developed methods where subsequently applied for the analysis of a nine generations AIL pedigree obtained after crossing two chicken lines divergently selected for body weight. Nine QTL described in a F2 population were replicated in the AIL pedigree, and our strategy to use both genotype and phenotype information from all individuals in the entire pedigree clearly made efficient use of the available genotype information provided in AIL.

Diss. (sammanfattning) Uppsala : Univ., 2007. / Härtill 7 uppsatser.

Diss. (sammanfattning) Linköping : Linköpings universitet, 2008. / Härtill 6 uppsatser.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2006. / Härtill 6 uppsatser.

Diss. (sammanfattning) Umeå : Umeå universitet, 2010.

<p>Understanding the cold acclimation process in plants may help us develop genetically engineered plants that are resistant to cold. The key factor in understanding this process is to study the genes and thus the gene regulatory network that is involved in the cold acclimation process. Most of the existing approaches1-8 in deriving regulatory networks rely only on the gene expression data. Since the expression data is usually noisy and sparse the networks generated by these approaches are usually incoherent and incomplete. Hence a new approach is proposed here that analyzes the promoter regions along with the expression data in inferring the regulatory networks. In this approach genes are grouped into sets if they contain similar over-represented motifs or motif pairs in their promoter regions and if their expression pattern follows the expression pattern of the regulating gene. The network thus derived is evaluated using known literature evidence, functional annotations and from statistical tests.</p>

<p>Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and</p><p>management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.</p>

No description available.

<p>Endometrial adenocarcinoma (EAC) is the fourth leading cause of carcinoma in woman worldwide, but not much is known about genetic factors involved in this complex disease. During the EAC process, it is well known that losses and gains of chromosomal regions do not occur completely at random, but partly through some flow of causality. In this work, we used three different algorithms based on frequency of genomic alterations to construct 27 tree models of oncogenesis. So far, no study about applying pathway models to microsatellite marker data had been reported. Data from genome–wide scans with microsatellite markers were classified into 9 data sets, according to two biological approaches (solid tumor cell and corresponding tissue culture) and three different genetic backgrounds provided by intercrossing the susceptible rat BDII strain and two normal rat strains. Compared to previous study, similar conclusions were drawn from tree models that three main important regions (I, II and III) and two subordinate regions (IV and V) are likely to be involved in EAC development. Further information about these regions such as their likely order and relationships was produced by the tree models. A high consistency in tree models and the relationship among p19, Tp53 and Tp53 inducible</p><p>protein genes provided supportive evidence for the reliability of results.</p>