High-throughput technologies in the life-sciences, from sequencing to microarrays, continue to produce exponentially increasing amounts of data and drive the need and inspiration for the development of new methods in the computational sciences to efficiently manage and mine these data and provide a better understanding of life from the molecular to the population level. Bioinformatics, computational and systems biology, and the new -omics sciences, from genomics to metabolomics, all attempt to address these needs and better understand carbon-based computing, using silicon-based computing, with slightly different perspectives and emphases. Their success is intimately related to progress in mathematics and computer science, in particular in the areas of artificial intelligence, statistical machine learning, and optimization, in order to deal with very complex, high-dimensional, and noisy data.
The aim of the School is to introduce and expose the students to some of the contemporary problems and state-of-the-art solutions at the intersection of the life- and computational sciences. For this purpose, the School is organized into four themes, of approximately increasing spatial scale complexity: Chemoinformatics, Proteomics, Genomics, and Systems Biology. Each theme will comprise lectures and invited talks, as well ample time for discussion, over a period of one and a half day. Additional minicourses to cover computational methods in machine learning and optimization will be given during the afternoons. |