Instructors: · Michael Schroeder (MS) · Michael Hiller (MH) · Lutz Brusch (LB)

Synopsis:
To interpret high-throughput experiments, it is vital to understand the function and interactions of the identified molecules. This course introduces functional annotation with controlled vocabularies as well as basic concepts and algorithms for the analysis of network topology and dynamics. It also covers basic concepts and methods in comparative and functional genomics.

The student will know how to model biomedical knowledge with hierarchical vocabularies and how to reason over it. You will learn about the main data sources for networks and their associated characteristics, graph-theoretic concepts to describe and model networks as well as algorithms.

Prerequisites:

• Calculus 101,
• Vector algebra,
• Discrete Algorithms for Computational Biology

Format:
4 SWS (2V+2U)

Exam
written

Special remarks:
The course is part of the Computational Biology minor program.

Syllabus:

• Week 1-6 : (MS)
  • Introduction to biomedical ontologies
  • Functional annotation, enrichment analysis
  • Ontologies and textmining
  • Protein interaction networks: Data sources and quality
  • Betweeness, cluster index, shortest paths, scale free networks
  • Visualising protein interaction networks
  • Modules in networks
• Week 7-9 : (MH)
  • Comparative genomics, repeat landscape
  • methods to detect selection
  • functional genomics, enrichment tests of cis-regulatory regions
  • ENCODE project
• Week 10-14 : (LB)
  • Elementary flux modes and flux balance analysis for metabolic networks
  • Differential equation models of dynamic metabolism
  • Regulatory structures in signal transduction pathways
  • Dynamic responses to signals