Computational and Systems Biology I

15 credits

Syllabus, Master's level, 1MB511

A revised version of the syllabus is available.
Code
1MB511
Education cycle
Second cycle
Main field(s) of study and in-depth level
Molecular Biotechnology A1F, Technology A1F
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 27 April 2012
Responsible department
Biology Education Centre

Entry requirements

120 credits including Genome Biology

Learning outcomes

The purpose of the course is to provide insight into quantitative modelling of biological systems at the molecular and cellular level, as well as how they are used, analysed and developed. After passing the course, the student should be able to

  • describe how protein folding happens from both an energetic and a structural perspective
  • describe how protein structure can be determined using x-ray scattering or nuclear magnetic resonance (NMR) experiments
  • model macromolecular complexes on different time and length scales
  • model macromolecular structures with the help of experimental information
  • explain cellular processes by describing the interactions between macromolecules in a kinetic network
  • model networks of chemical reactions coupled with diffusion
  • present research results in a professional way and use modern multimedia techniques for the purpose.

Content

Macromolecules have intricate three-dimensional structures that can be decided with experiments such as X-ray scattering or nuclear magnetic resonance (NMR). The methods to determine the structure of macromolecules will be described and explained in lectures, literature and in one of the laboratory sessions. The physical basis to the structure and folding of biomolecules is treated in lectures, literature and computer exercises.

Modelling of macromolecular complexes is an important step towards understanding the different cellular processes. The course focuses on different examples of large complexes that consist of RNA and/or proteins. The method is based on the principle that information of different kinds can be combined to make efficient models. The theory is treated on some lectures, in the literature and computer exercises.

Most biochemical processes are so complex that one needs simplified models to describe them. Here, we introduce models for the dynamics in biochemical or genetic networks. Such models can be based on chemical reactions but also include diffusion and stochastic fluctuations. We will use the examples of enzymatic reactions, oscillators and biochemical switches to introduce analysis techniques that can be used to understand connected deterministic chemical reactions in noisy systems.

By having several oral presentations, the student will get the opportunity to practice presenting research results. Feedback will be given on the contents and the student's presentation technique.

Instruction

Lectures, seminars and computer exercises.

Assessment

The course is examined through written and oral project presentations based on the three compulsory laboratory sessions.

The lab reports correspond to 3 credits each, the oral presentations correspond to 2 credits each.

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