Tehnologije znanj za odkrivanje novih zdravilnih učinkovin: analiza in načrtovnje eksperimentov v visokozmogljivostni genetiki (Slovene)

With recently developed high-throughput technologies that allow us to gather biomedical data on genome-wide scale under a wide range of experimental conditions, scientific discovery has shifted from labour-intensive to computationally intensive task. The project will develop and apply a set of computational tools for inference of the mechanism of action of pharmacologically active substances in a model organism S. cerevisiae. In the application we will use a set of chemical-genomics profiles, that is, currently the most informative source on the interactions between drugs and genes. Data mining will be used to uncover the mechanism of drugs’ action. We will combine data analysis with in silico experiment planning techniques, and carry the proposals out on a robotic platform to increase the reliability of proposed hypotheses. The expected principal results of this projects are A) bioinformatics toolbox (data analysis through clustering and classification of complex, genome-wide profiles, experiment planning through active learning), B) identification of a set of marker genes/mutants with high information content to predict the mechanism of drugs’ action, and C) a prototype of a high-throughput experimental platform combining state-of-the-art technologies from genetics, laboratory robotics and computational analysis for rapid classification of molecules based on their chemical-genetic interactions. Chemical-genomics is a very young and promising field of functional genomics, requiring dedicated computational tools for their application. With current practical demonstrations in this field being presently at best rare, development and application of proposed knowledge technology tools for analysis and proposal of experiments in drug discovery should be regarded as highly original.

In the project we developed the most accurate method to date for measuring growth rate of yeast cells in colonies in a small area, which enables determination of fitness phenotype of individual strains. This development is important from the perspective of increase in the accuracy of some of the techniques used in high-throughput genetics, as well as in the design of live cells-based biosensors.

In the project we introduced new high-throughput genetics method not previously known in Slovenia, and through that enabled know-how important for competitiveness of pharmaceutical industry. The project is important also because of the training of postgraduate and undergraduate students in the fast-evolving area of functional genomics.