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270155 VU (Introduction to) Network analysis with Python (2021W)

3.00 ECTS (2.00 SWS), SPL 27 - Chemie
Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 20 participants
Language: German



18.10.-22.10.2021, 9-16:00, online (BBB)


Aims, contents and method of the course

Learning Outcomes

Students will gain an understanding of network science and learn various examples of where networks science can be applied in biochemistry and beyond. Students will get a feel for the type of networks that can be constructed to represent various (chemical) interactions and the network properties and modelling techniques that can be used to answer scientific research questions about those interactions. Furthermore, students will gain a gentle introduction to programming in Python in order to learn how to use the Networkx package for generating and analysing networks.

Course Content

• Introduction to graph theory and the calculation of structural network properties
• Discussion of different network types and examples of real networks
• Introduction to analysing networks in Python using the networkx package
• Discussion of good coding practices and writing pseudo code
• Genome-scale modelling of metabolic networks


The course will be divided into lectures and programming practicals. Each set of theory will be followed by examples on how to implement the theory in Python and a set of programming tasks. A combination of course participation, presentations and a programming assignment will be used to assess the material covered in the lectures and practicals.

To complete the practical tasks of this course, students will require access to a computer or laptop with a web browser. Programming tasks are designed to be hosted on a server which has the required software pre-installed and to which students enrolled in the course will be granted access.

The course will be instructed in English.

Assessment and permitted materials


• Participation in lectures and practicals
• Oral presentation of a research paper
• Python programming assignment
• Oral presentation of the programming assignment

Minimum requirements and assessment criteria

The course is designed to be an introduction to network science and to programming. Students are not expected to have prior programming experience. Course attendance (virtually via BBB) is mandatory. Students may be excused for no more than 6h (one day) unless otherwise agreed upon. 50% of the maximal points, as outlined below, must be attained to pass the course.

Marking Scheme

A maximum of 100 points can be achieved in the course. The 100 points are divided into:
• Participation in lectures and practicals: 20 points
• Research paper presentation: 20 points
• Programming assignment: 40 points
• Programming presentation: 20 points

Grades will be assigned as follows:
• 1 (excellent): 96-100
• 2 (good): 86-95 points
• 3 (satisfactory): 66-85 points
• 4 (sufficient): 51-65 points
• 5 (insufficient): 0-50 points

Examination topics

There are no restrictions with regards to the resources that students can use to complete their assignment(s), provided that all work submitted/ presented for assessment is original and has not been plagiarised. All assessments are based on the materials provided during the course.
The additional reading materials may be used to gain a deeper understanding of the content provided in the lectures and practicals. Student may be expected to read some of the additional material(s) in order to gain full marks.

Reading list

Reading Materials

A majority of the course material is based on the following textbook and chapters:

Barabasi, A.-L. & Marton, P. (2016) Network Science. Cambridge University Press. [available online at http://networksciencebook.com/]
• Chapter 1 -5, 8-9

The course is supplemented with examples from the following research papers and others:

Barabási, A., Oltvai, Z. (2004) Network biology: understanding the cell’s functional organization. Nature Reviews Genetics. 5, 101–113.

Barabási, A.-L., Gulbahce, N. & Loscalzo, J. (2011) Network Medicine: A Network-based Approach to Human Disease. Nature Review Genetics. 12,56-68.

Compeau, P.E.C., Pevzner, P.A. & Tesler, G. (2011) Why are de Bruijn graphs useful for genome assembly? Nature Biotechnology. 29, 987-991.

Firth, J.A., Hellewell, J., Klepac, P., Kissler, S., CMMID COVID-19 Working Group, Kurcharski, A.J., Spurgin, L.G. (2020) Using a real-world network to model localized COVID-19 control strategies. Nature Medicine. 26, 1616-1622.

Motter, A.E., Gulbahce, N., Almaas, E. & Barabási, A.-L. (2008) Predicting synthetic rescues in metabolic networks. Molecular Systems Biology. 4, 168.

Orth, J., Thiele, I. & Palsson, B. (2010) What is flux balance analysis? Nature Biotechnology. 28, 245–248.

Jeong, H., Mason, S.P., Barabási, A.-L. & Oltvai, Z.N. (2001) Lethality and centrality in protein networks. Nature, 411: 41-42.

Watts, D.J. & Strogatz, S.H. (1998) Collective dynamics of ‘small-world’ networks. Nature. 393, 440-442.

Association in the course directory

AN-2, BC-1, CHE II-1

Last modified: Mo 04.10.2021 00:06