Algorithms for Biological Networks

Mon, 2014-06-30 to Fri, 2014-07-04
In the third edition of this course, we will first give a brief overview of molecular biology, the advent of high-throughput measurement techniques and large databases containing biological knowledge, and the importance of networks to model all this. We will highlight a number of peculiar features of biological networks. Next, a number of basic network models (linear, Boolean, Bayesian) will be discussed, as well as methods of inferring networks from observed measurement data and of integrating various data sources and databases to refine networks. Once networks are derived they often serve as the cornerstone in the visualization, analysis and interpretation of high-throughput data; we will discuss a number of methods in this area. As an alternative to static networks, a number of alternative dynamic network models more suited for high-level simulation of cellular behaviour for will be introduced. Finally, we will give some examples of algorithms exploiting the networks found to learn about biology, specifically for inspecting protein interaction networks and for finding active sub networks.
250-1000 euro