Computational Approaches for Analyzing Complex Biological Systems (2010)

This course was taught at the University of Tübingen.

Course overview

Title of the course

Computational Approaches for Analysing Complex Biological Systems


Dr. Karsten Borgwardt, MPI Developmental Biology and MPI Biological Cybernetics and Dr. Oliver Stegle, MPI Developmental Biology and MPI Biological Cybernetics

Practical Session Tutor

Theofanis Karaletsos, MPI Developmental Biology and MPI Biological Cybernetics

Time Requirements

  • Monday, 13.9.2010 - Friday, 24.9.2010
  • 3 hours lecture per day: 9:00s.t. -11.30
  • 3 hours practical sessions per day:12:00 s.t.-14.15
  • (equivalent to 2 SWS(weekly hours persemester) lecture and 2 SWS practicals, 4 ECTS points)


Lecture Hall F 122 at Sand 6/7 (2/49 Pl./Inform.)


Oral Examination: Tuesday, 28th September 2010, beginning 9:00 AM, in room 1.B.02 in Spemannstr. 41.

Formal Credit

  • 4 ECTS points
  • Compulsory Chosen Module Bioinformatics (Master)
  • Practical Computer Science (Diploma)


Elementary knowledge of maths, no other required knowledge


In the course of the past few years, research on biological systems such as co-expression networks, protein-interaction networks and metabolomic networks has evolved into a core topic for bioinformatics. The increasing importance of complex systems in biology led to a significant need for novel algorithms to analyse and model biological systems.

In the first part of the lecture algorithms for the analysis of biological networks by means of graph comparison and pattern recognition in graphs will be presented. Kernel methods for the analysis of biological networks and an introduction into data-mining algorithms for motif search in these networks constitute the main focus of this part. The second part of the lecture is devoted to the study of probabilistic algorithms and models for the analysis of static and time-dependent networks. This part of the lecture will include methods for the statistical modeling of time-series.

This lecture is intended for students of bioinformatics and computer science with an interest in algorithmic and statistical methods for the analysis and modeling of complex systems with a focus on biology.



Course schedule and contents

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