Delft University of Technology (TUD), Delft, the Netherlands
• Date: 23-27 May 2016
• Course coordinators: Dick de Ridder (WUR), Jeroen de Ridder (TUD)
• Website: http://biosb.nl/education/course-portfolio/course-algorithms-for-biologi...
PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed.
In the fourth 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.
The course website, including full course programme, can be found at http://helix.ewi.tudelft.nl/nbic/abn/.
BioSB Course Programme
The Education Portfolio of BioSB can be found at http://biosb.nl/education/course-portfolio/.
For more information about the course you can contact Celia van Gelder at firstname.lastname@example.org.