Teaching Overview

Our research unit offers courses and seminars for several study programs, including the Bachelor of Molecular Biomedicine, Bachelor of Mathematics, the Master of Immunobiology, and the Master of Mathematics (all courses can be found on BASIS):

Summer Term

Workload     2 SWS, 3 ECTS
Exam            Will be announced
Workload     2 SWS, 3 ECTS
Exam            Will be announced

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

Workload     2 SWS, 3 ECTS
Exam            Written

Mathematical and computer-assisted modelling of biological systems 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.

Workload     2 SWS, 3 ECTS
Exam            Oral

Winter Term

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).

Workload      4 SWS, 7 ECTS
Exam             Oral or written

Students should learn the role and the principle approaches applied in systems immunology, bioinformatics and big data science in generaland particular in respect to the immune system including the relevant methodology applied in the field.


  • Basic principles of the methods applied in systems immunology
  • Biological interpretation of high throughput data and the visualization of large dataset
  • Application of learned skills to existing datasets addressing immunological questions

Workload     2 SWS, 4.5 ECTS
Exam            Written

The course will provide a deep understanding and detailed overview of a current research focus from the area
of applied probability. Ability to verify the validity of propositions from original literature independently and to question research results critically. Competence to engage in independent study of current research topics

The topics to be covered will be announced at the end of the semester prior to commencement of the course. Possible topics include:

  • Stochastic finance (Option pricing, econometrics, optimal stopping)
  • Monte Carlo methods (Numerical methods for SDE, MCMC, filtering)
  • Branching processes and models from population biology
  • Probability on graphs and networks (Random graphs, models of statistical mechanics, stochastic algorithms)

Workload    4 SWS, ECTS 7
Exam          Oral

Topic-wise it closely resembles the book "Lectures on Convex Optimization" by Nesterov.


  • smooth/non-smooth convex optimization
  • methods of second order
  • primal-dual

Additionally, we offer different graduate seminars on current topics. Check them out on BASIS.

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