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Post-genotnie research focusses on the functional analysis of genomes and the complete annotation of cellular processes. In the genetic model system yeast, systematic screens have been devised for this purpose. Screening for synthetic lethality, the cell death resulting from deletion of two genes with compensatory functions, has greatly contributed to the elucidation of cellular pathways and their functional interactions. The resulting data quantity is large and challenging to interpret. In addition, the process of screening the entire yeast genome is still ongoing. Thus, there is a need for sophisticated computational methods that analyze and predict synthetic lethal interactions between genes. This doctoral thesis presents two data integration approaches based on genomic and proteomic information that deal with i) the assignment of novel genes to biological processes using synthetic lethality data and ii) the prediction of synthetic lethality. The first part of the work displays a elustering method for the characterization of genetic interactions found in synthetic lethal screens. Fitting a Gaussian Mixture Model on a set of biological input variables identifies target genes with elose functional relationships to the genes used for screening. Testing the procedure on two biological scenarios, mitotic spindle migration and TOR2 (target of rapamycin) signaling, yields lists of genes with assumed functions in these processes. Experimental validation of the candidates for spindle migration confirms the modeling outcome, proving the potential of the method. The second part uses elassification methods for the prediction of synthetic lethality from genomic data. A comparative assessment evaluates the models logistic regression, naive Bayes and random Forest based on prediction accuracy and error rates. During model training, a realistic modeling strategy is applied that simulates the conditions for new screens. The best resulting model predicts interactions for the TORI kinase, which prove realistic and feasible. The thesis comprises a useful set of tools for synthetic lethal data analysis, that generate biologically relevant outcomes. Their application contributes to the exploration of the complex genetic network in yeast and possibly other organlsms.

Data integration methods for the analysis of synthetic lethality