Analysis of Biological Networks

The lectures from earlier years are placed on this page to give the student an impression of what is coming.  The lectures will be updated as the date approaches

Week 1

Lecture 1: Dynamic High Throughput Data in Biology (12.10.09)[PPT]

Lecture 2: Statistical analysis of Dynamic High Throughput Data (13.10.09) [PPT]

Reading material

Week 2

Lecture 3: Network Algorithms  Paths (19.10.09)

Lecture 4: Network Algorithms  Connectivity (20.10.09)

Graph algorithm lecture notes

Exercises & Model Solution

Week 3

Lecture 5: Network Algorithms  Flow (26.10.09)

Lecture 6: Combinatorial Methods of Network Comparison (27.10.09)

Exercises & Model Solution

Week 4

Lecture 7: Probability Theory of Networks (2.11.09)

Lecture 8: Reconstruction Problems in Networks (3.11.09)

Week 5

Lecture 9: Enumeration of Networks (9.11.09)

Lecture 10: Sampling Networks (10.11.09)

Week 6

Lecture 11: Network Inference (16.11.09)

Lecture 12: Network Robustness (17.11.09)

Reading material: A boosting approach to structure learning of graphs with and without prior knowledge

Modelling transcriptional regulation using Gaussian processes (not part of course material, but included as reference for the keen student)

Gaussian Processes for Machine Learning

Graphs, Networks, and Algorithms (only chapter 12 used in course)

Exercises & Model Solution

Week 7

Lecture 13: Evolution of Networks I (23.11.09) [PPT]

Lecture 14: Evolution of Networks II (24.11.09) [PPT]

Week 8

Lecture 15: Network Inference II (30.11.09) [PPT]

Lecture 16: Computational Biology of Networks (1.12.09) [PPT]