A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Knowledge derivation and data mining strategies for probabilistic functional integrated networks

PhDOne of the fundamental goals of systems biology is the experimental verification of the interactome: the entire complement of molecular interactions occurring in the cell. Vast amounts of high-throughput data have been produced to aid this effort. However these data are incomplete and contain high levels of both false positives and false negatives. In order to combat these limitations in data quality, computational techniques have been developed to evaluate the datasets and integrate them in a systematic fashion using graph theory. The result is an integrated network which can be analysed using a variety of network analysis techniques to draw new inferences about biological questions and to guide laboratory experiments. Individual research groups are interested in specific biological problems and, consequently, network analyses are normally performed with regard to a specific question. However, the majority of existing data integration techniques are global and do not focus on specific areas of biology. Currently this issue is addressed by using known annotation data (such as that from the Gene Ontology) to produce process-specific subnetworks. However, this approach discards useful information and is of limited use in poorly annotated areas of the interactome. Therefore, there is a need for network integration techniques that produce process-specific networks without loss of data. The work described here addresses this requirement by extending one of the most powerful integration techniques, probabilistic functional integrated networks (PFINs), to incorporate a concept of biological relevance. Initially, the available functional data for the baker’s yeast Saccharomyces cerevisiae was evaluated to identify areas of bias and specificity which could be exploited during network integration. This information was used to develop an integration technique which emphasises interactions relevant to specific biological questions, using yeast ageing as an exemplar. The integration method improves performance during network-based protein functional prediction in relation to this process. Further, the process-relevant networks complement classical network integration techniques and significantly improve network analysis in a wide range of biological processes. The method developed has been used to produce novel predictions for 505 Gene Ontology biological processes. Of these predictions 41,610 are consistent with existing computational annotations, and 906 are consistent with known expert-curated annotations. The approach significantly reduces the hypothesis space for experimental validation of genes hypothesised to be involved in the oxidative stress response. Therefore, incorporation of biological relevance into network integration can significantly improve network analysis with regard to individual biological questions