Credits: 5

Schedule: 08.01.2019 - 21.02.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

III (Spring)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After the course students will have a comprehensive understanding of fundamental methodological concepts underlying modeling of biological networks and systems. Students will learn to choose appropriate modeling methods for a variety of small- and large-scale problems as well as for different types of experimental data. Students will learn to apply various computational and statistical modeling methods in real interdisciplinary biological problems and have sufficient knowledge to explore the topic further.

Content (valid 01.08.2018-31.07.2020): 

Mathematical and statistical models of biological molecular-level networks, such as gene regulation, epigenetics, signaling, and metabolism. Models covered in the course include stochastic chemical reaction networks, ordinary differential equations, Bayesian and dependency networks, regression models and random networks. Statistical methods for inference of networks from data, and prediction using the  models. Mainly probabilistic and machine learning models.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Examination and exercises/assignment problems.

Workload (valid 01.08.2018-31.07.2020): 

24 + 24 (4 + 4)

Study Material (valid 01.08.2018-31.07.2020): 

To be specified in MyCourses at the start of the course.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Replaces former course CS-E5880 / T-61.5110 Modeling Biological Networks.

Prerequisites (valid 01.08.2018-31.07.2020): 

Basic mathematics, statistics and computer science/programming courses. Basic bioinformatics courses help.

Grading Scale (valid 01.08.2018-31.07.2020): 



Registration and further information