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
The objective of the project described in this thesis was to study the complex induction of extracellular proteases in the filamentous fungus Aspergillus niger using information gathered with... Show moreThe objective of the project described in this thesis was to study the complex induction of extracellular proteases in the filamentous fungus Aspergillus niger using information gathered with functional genomics technologies. A special emphasis is given to the requirements for performing a successful systems biology study and addressing the challenges met in analyzing the large, information-rich data sets generated with functional genomics technologies. The role that protease activity plays in strain and process development of A. niger and other aspergilli is reviewed. The influence of several environmental factors on the production of extracellular proteases of A. niger in controlled batch cultivations was studied. Samples generated in this study were used for analysis with different functional genomics technologies. With a shotgun proteomics approach the A. niger secretome under different experimental conditions was determined. Furthermore, the effect of different quantitative phenotypes related to protease or glucoamylase activity on the information content of a metabolomics data set was investigated. Finally, the clustering of co-expressed genes is described. First, a set of conserved genes was used to construct gene co-expression networks. Subsequently, all protein-coding A. niger genes, including hypothetical and poorly conserved genes, were integrated into the co-expression analysis. Show less
