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Course: Computational Systems Biology

Mathematical models are widely used to achieve a mechanistic understanding of complex biological processes. To develop this models, a broad spectrum of mathematical and computational tools are required, including statistics, numerics, optimization and programming. In this course, will provide an introduction to state-of-the-art approaches for computational modelling:

  • Deterministic and stochastic modelling in systems biology
  • Statistical modelling of experimental data
  • Parameter optimization (e.g. gradient calculation and trust-region methods
  • Parameter uncertainty analysis (e.g. MCMC methods)
  • Model selection (e.g. AIC, BIC and Bayes factor)

In addition to the established methods, we will point out open problems and challenges.
The lectures are complemented by hands-on exercises. Students will learn to implement the different tools in Python, as well as to use established tools.
The course is part of the Master’s program in Mathematics and open for PhD students in the Bonn International Graduate School in Mathematics (BIGS).
Work load     4 SWS; 7 ECTS
Time             Monday & Thursday, 10.15-11.45 am
Exam            oral or written


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