The combination of collision-activated dissociation, (CAD) and electron capture dissociation, (ECD) yielded a 125% increase in protein identification. The S-score was developed for measuring the information content in MS/MS spectra. This measure made it possible to single out good quality spectra that were not identified by a search engine. Poor quality MS/MS data was filtered out, streamlining the identification process. A proteomics grade de novo sequencing approach was developed enabling to almost completely sequence 19% of all MS/MS data with 95% reliability in a typical proteomics experiment. A new tool, Modificomb, for identifying all types of modifications in a fast, reliable way was developed. New types of modifications have been discovered and the extent of modifications in gel based proteomics turned out to be greater than expected. PhosTShunter was developed for sensitive identification of all phosphorylated peptides in an MS/MS dataset. Application of these programs to human milk samples led to identification of a previously unreported and potentially biologically important phosphorylation site. Peptide fragmentation has been studied. It was shown emphatically on a dataset of 15.000 MS/MS spectra that CAD and ECD have different cleavage preferences with respect to the amino acid context. Hydrogen rearrangement involving z• species has been investigated. Clear trends have been unveiled. This information elucidated the mechanism of hydrogen transfer. Partial side-chain losses in ECD have been studied. The potential of these ions for reliably distinguishing Leu/Iso residues was shown. Partial sidechain losses occurring far away from the cleavage site have been detected. A strong correlation was found between the propensities of amino acids towards peptide bond cleavage employing CAD and the propensity of amino acids to accept in solution backbone-backbone H-bonds and form stable motifs. This indicated that the same parameter governs formation of secondary structures in solution and directs fragmentation in peptide ions by CAD.

The aim of this project was to present a cost-efficient quality control routine to ensure thefunctionality of HS-Lenti RT Activity Kit, an ELISA-based kit produced by Cavidi AB, thatscreens for lentiviruses such as HIV. Two main methods for quality control are presented inthis report; acceptance sampling and Statistical Process Control (SPC). Acceptance samplingis a process where only parts of a batch are tested in order to determine whether or not thewhole batch should be accepted or rejected. SPC centers around monitoring an ongoingprocess by using statistical measures and charts which visualize variations in these measuresover time. Initially, using an acceptance sampling plan is recommended as the primaryapproach. SPC charts can then be set up using the data generated from the acceptancesampling, and be used in parallel with the acceptance sampling for some time until they canbe implemented to a wider extent. This report also presents different options forimmunoassay data processing. Bayesian methods of estimating analyte concentrations inunknown samples are highlighted as promising candidates in improving the performance andusability of the kit. The report also includes a customer requirements analysis, based on aconducted survey, that investigates the demands researchers within Uppsala University placeon products similar to HS-Lenti RT Activity Kit. The data which the analysis is based on wasobtained from an online questionnaire and three interviews. An ethical analysis regarding thequality control approach and survey is included as well.

Licentiatavhandling Uppsala, Univ : 2004.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2008. / Härtill 5 uppsatser.

Chemnitz, Techn. Univ., Diplomarb., 2003.

Techn. Universiẗat, Diss., 2001--München.

<p>Clustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the graph clustering iteratively, with a previously computed cluster as input set. The innovation is that it focuses on another feature of the data in each step and clusters the data according to this feature. Common hierarchical approaches cluster e.g. in biology, a set of genes according to the similarity of their sequences. The clustering then reflects a partitioning of the genes according to their sequence similarity. The approach introduced in this thesis uses many features of the same objects. These features can be various, in biology for instance similarities of the sequences, of gene expression or of motif occurences in the promoter region. As part of this thesis not only the algorithm itself was implemented and evaluated, but a whole software also providing a graphical user interface. The software was implemented as a framework providing the basic functionality with the algorithm as a plug-in extending the framework. The software is meant to be extended in the future, integrating a set of algorithms and analysis tools related to the process of clustering and analysing data not necessarily related to biology.</p><p>The thesis deals with topics in biology, data mining and software engineering and is divided into six chapters. The first chapter gives an introduction to the task and the biological background. It gives an overview of common clustering approaches and explains the differences between them. Chapter two shows the idea behind the new clustering approach and points out differences and similarities between it and common clustering approaches. The third chapter discusses the aspects concerning the software, including the algorithm. It illustrates the architecture and analyses the clustering algorithm. After the implementation the software was evaluated, which is described in the fourth chapter, pointing out observations made due to the use of the new algorithm. Furthermore this chapter discusses differences and similarities to related clustering algorithms and software. The thesis ends with the last two chapters, namely conclusions and suggestions for future work. Readers who are interested in repeating the experiments which were made as part of this thesis can contact the author via e-mail, to get the relevant data for the evaluation, scripts or source code.</p>

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Proteins are essential in all living organisms. They are involved in various critical activities and are also structural components of cells and tissues. Lactose permease a membrane protein has become a prototype for the major facilitator super family and utilises an existing electrochemical proton gradient to shuttle galactoside sugars to the cell. Therefore it exists in two principle states exposing the internal binding site to either side of the membrane. From previous studies it has been suggested that protonation precedes substrate binding but it is still unclear why this has to occur in the event of substrate binding. Therefore this study aimed to bridge this gap and to determine the chemical characteristics of the transport pathway. Molecular dynamics simulation methods and specialised simulation hardware were employed to elucidate the dependency of substrate binding on the protonation nature of Lactose permease. Protein models that differed in their conformation as well as their protonation states were defined from their respective X-ray structures. Targeted molecular dynamics was implemented to drive the substrate to the binding site and umbrella sampling was used to define the free energy of the transport pathway. It was therefore suggested that protonation for sugar binding is due to the switch-like mechanism of Glu325 in the residue-residue interaction (His322 and Glu269) that leads to sugar binding only in the protonated state of LacY. Furthermore, the free energy profile of sugar transport path way was lower only in the protonated state which indicates stability of sugar binding in the protonated state.

Celiac disease (CD) is a chronic autoimmune disease, caused by the consumption of gluten in genetically predisposed individuals. Celiac patients develop many clinical features include; weight loss, diarrhea, and Intestinal damage, and if left untreated, CD patient may face an increased risk of malignancies. Materials and methods403 patient were admitted to the study. These patients were divided into three groups; celiac cases, controls, and latent celiac cases. Gene expression analysis was performed for intestinal biopsies and blood samples (leukocytes) using a quantitative PCR technique. The second section of the study was studying the effect of PRODH enzyme on Drosophila Melanogaster intestines. To achieve that PRODH enzyme and different amino acids were added to the fly food.  One way ANOVA and Wilcoxon tests were applied to find out the significant genes. ResultsMost of the differentially expressed genes in celiac disease are involved in the inflammatory response. However, many genes have significantly altered expression in the latent celiac group but not altered significantly in CD group. These genes are CXCL1, IL15RA, IL2RB, MAPK11, and TGM2. They are involved in the TNF signaling pathway and in inflammatory cytokines. It was noticed that in celiac disease there is a significant alteration in PRODH expression in the intestines, and the addition of PRODH enzyme to glutamine has a similar effect on the intestinal gene expression as gluten does. ConclusionWe can conclude that Non-HLA genes are important in activating the immune system, increasing proline level, and developing the clinical features of celiac disease. Secondly,  Proline metabolism has an important role in tumor suppression and in augmenting tumor growth, which makes it an important therapeutic target in tumor therapy.