Universität Wien FIND

270155 VU (Introduction to) Network analysis with Python (2020W)

3.00 ECTS (2.00 SWS), SPL 27 - Chemie
Prüfungsimmanente Lehrveranstaltung

Unterrichtssprache Englisch


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 25 Teilnehmer*innen
Sprache: Deutsch



The course will commence on the 19th of October. The course is planned to run weekly, for 8 weeks, on Mondays from 5-7pm and Thursdays from 5-7pm. Lectures will be livestreamed via Moodle (BBB).


Ziele, Inhalte und Methode der Lehrveranstaltung

This course will be taught in English.

Learning Outcomes:
Students will gain an understanding of network science and learn various examples of where networks science can be applied in biochemistry. 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 chemical interactions.

Furthermore, students will gain a gentle introduction to programming in Python and will learn how to generate, analyse and model various types of networks in Python. At the end of the course students will be able to write their own functions to carry out computational analyses. Students will be given a taste of how programming can be used to analyse large data set in an efficient manner.

Course Content:
The course will provide an introduction to programming in python with examples from network science. Calculations of various network properties (e.g. shortest path, degree distribution, clustering, centrality) will be discussed and examples of how to perform them in Python will be provided.

Different types of networks (e.g. random, scale-free and small-world networks) will be discussed and the different types of interactions that they can represent (e.g. protein structure and protein-protein interactions, metabolic networks, disease networks) will be explored by means of examples.

An introduction to different ways to model network interactions, including boolean and constraint-based modelling techniques, will be provided. Students will learn to apply these techniques using various Python packages including networkx, PyBoolNet and cobrapy.

The course will be divided into lectures and programming practicals. Each set of theory (2h lecture) will be followed by examples on how to implement the theory in Python (2h practical).

A combination of course participation, presentations and a short 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. Students may also complete their programming tasks on a personal computer if they wish to do so and if they can meet all of the installation requirements.

Art der Leistungskontrolle und erlaubte Hilfsmittel

• Participation in lectures and practicals
• Oral presentation of a chosen scientific research paper
• Short programming assignment on the covered material (students will submit their code and provide a short explanation for how they solved the assigned task)

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 in the course. The additional reading materials may be used to gain a deeper understanding of the content provided in the lectures and practicals.

Mindestanforderungen und Beurteilungsmaßstab

The course is designed to be an introduction to network science and programming. Students are not expected to have prior programming experience.

Course attendance (virtually or physically, depending on circumstances) is mandatory. Students must attend at least 70% of the total number of lectures and practicals and may be excused for no more than 9h.

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: 10 points
• Research paper presentation: 30 points
• Programming Assignment: 60 points
◦ Code: 30 points
◦ Explanation of the code: 30 points

Grades will be assigned as follows:
• 1 (excellent): 100-86
• 2 (good): 71-85 points
• 3 (satisfactory): 61-70 points
• 4 (sufficient): 51-60 points
• 5 (insufficient): 0-50 points


All assessments are based on the materials provided in the course. The additional reading materials may be used to gain a deeper understanding of the content provided in the lectures and practicals.


The course does not directly align with any textbook, however, the course will draw primarily from the following literature. Provided reading reading materials can be used to gain a deeper understanding of the course content and to expand beyond the learning outcomes:

Barabasi, A.-L. & Marton, P. (2016) Network Science. Cambridge University Press.

Newman, M. (2010) Networks: An Introduction. Oxford University Press.

Lutz, M. (2013) Learning Python. O’Reilly and Associates.

Research Papers:
Barabasi, A.-L. & Oltvai, Z.N. (2004) Network biology: understanding the cell’s functional organization. Nature Review Genetics, 5: 101-113. DOI: https://doi.org/10.1038/nrg1272

Jeong, H., Tomber, B., Albert, A., Oltvai, Z.N. & Barabasi, A.-L. (2000) The large-scale organization of metabolic networks. Nature, 407: 651-654. DOI: https://doi.org/10.1038/35036627

Jeong, H., Mason, S.P., Barabasi, A.-L. & Oltvai, Z.N. (2001) Lethality and centrality in protein networks. Nature, 411: 41-42. DOI: https://doi.org/10.1038/35075138

Orth, J.D., Thiele, I., & Palsson, B.O. (2010) What is flux balance analysis? Nature Biotechnology, 28: 245-248. DOI: https://doi.org/10.1038/nbt.1614

Wynn, M.L., Consul, N., Merajver, S.D. & Schnell, S. (2012) Logic-based models in systems biology: a predictive and parameter-free network analysis method. Integrative Biology, 4: 1323-1337. DOI: https://doi.org/10.1039/c2ib20193c

Watts, D.J. & Strogatz, S.H. (1998) Collective dynamics of ‘small-world’ networks. Nature, 393: 440-442. DOI: https://doi.org/10.1038/30918

Zuordnung im Vorlesungsverzeichnis

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

Letzte Änderung: Fr 25.09.2020 12:49