The use of existing medications for diseases they were not originally developed for is called drug repositioning. A popular drug repositioning method to find new drugs against specific cancer types... Show moreThe use of existing medications for diseases they were not originally developed for is called drug repositioning. A popular drug repositioning method to find new drugs against specific cancer types is to search for drugs which are expected to bring back the gene expression activity of cancer cells to that of healthy cells (‘normalization’). One of the main research goals of this thesis was to investigate of this method could also be used on the gene expression profiles of individual tumors, enabling personalization of drug repositioning candidates for each patient. We initially had some success with this approach but this eventually lead to a systematic validation of the underlying principle using almost 10,000 tumor samples across 18 different tumor types. Unfortunately, the predictive power of the method was found to be much lower than previously reported and the part that remained could be nullified by correcting the gene expression profiles of the drugs for the downstream effects of reduced cell division. These results indicate that the current use of the method does not result in drug repositioning candidates specific for a tumor type but is only able to select generally cell-toxic drugs. Show less
This thesis demonstrates the application of bioinformatics to investigate the mechanisms that are implicated in Huntington’s Disease (HD). HD is an inherited neurodegenerative disorder and although... Show moreThis thesis demonstrates the application of bioinformatics to investigate the mechanisms that are implicated in Huntington’s Disease (HD). HD is an inherited neurodegenerative disorder and although the cause of the disease is known since 1993 we are still lacking a cure or treatment that can effectively treat the symptoms of HD. In order to tackle such a complicated case study, we followed a multidisciplinary approach to exploit the expertise and knowledge of people with diverse scientific background (chapter 2). This blend of disciplines facilitates constant collaboration between bioinformaticians, wet lab technicians, biologists, computer engineers and data scientists. A collaborative eScience model is proposed as a way to combine state-of-the-art computation analysis and laboratory work (chapter 3). At the same time, we explored methods to preserve the results, materials and methods involved in the experiment to increase the reproducibility and reusability of our research (chapter 4). In chapter 5 we identified disease signatures in blood that are functionally similar to signatures in brain. These are proposed as candidate biomarkers to be used as a monitoring tool for the state of the disease in brain, but also as a means to determine whether a treatment is successful or not. Show less
This dissertation mainly focuses on interdisciplinary approaches for biomedical knowledge discovery. This required special efforts in developing systematic strategies to integrate various data... Show moreThis dissertation mainly focuses on interdisciplinary approaches for biomedical knowledge discovery. This required special efforts in developing systematic strategies to integrate various data sources and techniques, leading to improved discovery of mechanistic insights on human diseases. Chapter one looks at the possibility in which combining various bioinformatics-based strategies can significantly improve the characterization of the OPMD mouse model. We discuss that this approach in knowledge discovery, on the basis of our extensive analysis, helped us to shed some light on how this model system relates to OPMD pathophysiology in human. In Chapter two, we expand on this combinatory approach by conducting a cross-species data analysis. In this study, we have looked for common patterns that emerge by assessing the transcriptome data from three OPMD model systems and patients. This strategy led to unravelling the most prominent molecular pathway involved in OPMD pathology. The third chapter achieves a similar goal to identify similar molecular and pathophysiological features between OPMD and the common process of skeletal muscle ageing. Engaging in a study in which the focus was made on the universality of biological processes, in the light of evolutionary mechanisms and common functional features, led to novel discoveries. This work helped us uncover remarkable insights on molecular mechanisms of ageing muscles and protein aggregation. Chapters four and five take a different route by tackling the field of computational biology. These chapters aim to extend network inference by providing novel strategies for the exploitation and integration of multiple data sources. We show that these developments allow us to infer more robust regulatory mechanisms to be identified while translations and predictions are made across very different datasets, platforms, and organisms. Finally, the dissertation is concluded by providing an outlook on ways the field of systems biology can evolve in order to offer enhanced, diversified and robust strategies for knowledge discovery. Show less
In general, biological and chemical causes for harmful effects were studied through bioinformatics and cheminformatics efforts. A database of human genetic variants in G protein-coupled receptors... Show moreIn general, biological and chemical causes for harmful effects were studied through bioinformatics and cheminformatics efforts. A database of human genetic variants in G protein-coupled receptors was constructed, and differences between neutral and harmful variants were studied. A database of compounds with their mutagenicity data was constructed, and substructures were extracted that distinguish between Ames positive and Ames negative compounds. 6. Keywords (At most 10, in English), preferably from the thesaurus in use within your discipline. Do not use very general terms. cheminformatics, chemoinformatics, bioinformatics, databases, data mining, drug discovery, SNPs, polymorphisms, substructures. Show less
