Written exam (oral exam with a small number of participants), practice certificate as an exam requirement. Practice points can be included as bonus points in the exam evaluation.

This application-oriented course imparts essential knowledge on the dynamic modeling of biological systems. This opens up numerous application areas, such as optimizing biotechnological processes, personalized medicine, preclinical studies, and understanding current systems biology research. In addition, students learn to work with the programming environment Tellurium, which is based on the Python programming language and brings with it the declarative systems biology modeling language called “Antimony.”

Students will learn the basic approach to building biochemical reaction models and concepts for analyzing dynamic network states. Data sources and forms of representation for the models will be covered. Emphasis is placed on physical constraints and implicit assumptions, such as conservation of mass, types of biochemical reactions, principles of enzyme catalysis, application and derivation of kinetic equations, open and closed systems and the influence of reversible reactions on the overall system, and processes occurring on different time scales to obtain plausible models.

Furthermore, energy conservation, the influence of cofactors and redox potentials, and regulatory mechanisms in biochemical systems are considered. Students learn how to classify the correctness of simulation results by estimating the magnitudes of cellular components. Students will gain an overview of numerical methods relevant to simulation and learn how to simulate models dynamically. Suitable graphical representations for the analysis of simulation results are discussed. Finally, the principles learned are applied to selected metabolic pathways, and their coupling concerning the cellular scale is discussed.

The content does not build directly on the lecture Systems Biology I so this course can be attended independently.

Students learn to apply methods of mathematical modeling to systems biology models.
This includes
- the creation of models of biochemical reaction networks,
- the simulation and analysis of the dynamic responses of these models,
- as well as basic programming techniques for solving systems biology problems.

1. Bernhard Ø. Palsson 2011. Systems Biology: Simulation of Dynamic Network
States. Cambridge University Press, New York, ISBN 978-1-107-
00159-6.
2. David S. Goodsell. 2009. The Machinery of Life. 2. Ausgabe, Springer-
Verlag, ISBN 978-0387849249.
3. Jan Koolman und Klaus-Heinrich Roehm. Color Atlas of Biochemistry.
2. Ausgabe, Thieme, 2005.