Students, who are successful in the course will be able to:
- implement basic graph theoretic algorithms such as MST, shortest paths, maximal connected components,
- create code and statistical tools that identify frequent and signifcant motifs in biological networks,
- preprocess experimental data (e.g. microarray, CHiP-seq, MS-MS) and convert it into a putative network (or integrate these with an existing network),
- measure and analyze the sensitivity of a system to input (i.e. assess the robustness of a system),
- perform kinetics calculations to find the behaviour (time response, steady state) of a biochemical dynamical system,
- visualize large networks and analyze the degree distribution to find out scaling and clustering propertires of the network.