Teaching

Genomics and Bioinformatics

Sequencing has revolutionized life sciences and is nowadays an irreplaceable tool. This course provides a comprehensive introduction to genomics and bioinformatics:

• Exome, transcriptome and epigenome sequencing
• Data processing (e.g., sequence alignment and quantification)
• Data visualization (e.g., linear and nonlinear dimension reduction)
• Data analysis (e.g., statistical methods and deep learning)
• Clinical applications

The course is part of the Bachelor’s program in Molecular Biomedicine and organized in collaboration with the lab of Prof. Dr. Joachim Schultze.
 
Details:
Work load     2 SWS, 3 ECTS
Time             Tuesday, 8.15-9.45 am
Exam            written

Introduction to Systems Biology

Mathematical and computer-assisted modelling of biological systems is possible from the life sciences are now indispensable. Models enable e.g. to understand complex processes, to test hypotheses, to predict time courses and experiments. Applications range from basic research to from industrial applications to clinical practice.
This lecture will provide an introduction to basic concepts and modern methods of systems biology. In particular, we will deal with biochemical reaction networks and deal with the following topics:

• Modelling of biochemical reaction networks (e.g. in signal transductions)
• Analysis of model properties
• Implementation and simulation of models in the computer
• Adaptation of model parameters to experimental data

The lectures are complemented by hands-on exercises.
The course is part of the Bachelor’s program in Molecular Biomedicine.

Details:
Work load     2 SWS, 3 ECTS
Time             to be announced
Exam            oral

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).
 
Details:
Work load     4 SWS, 7 ECTS
Time             Monday & Thursday, 10.15-11.45 am
Exam            oral or written