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Metabolic Network Analysis (BM)


Admission requirements

Elementary calculus, some basic knowledge of linear algebra is useful (e.g. matrix addition, matrix multiplication).


The course discusses rationales and methods for the mathematical modelling of large biochemical networks, metabolic networks in particular, and the subsequent contraint-based analysis of their dynamic properties. We introduce the concepts of the stoichiometric matrix and flux vector and show what information can already be deduced from them, e.g. concerning possible steady state flux vectors for the system: extreme pathways, elementary modes and the relationships among the two. Several algorithms will be explained for computing them, together with hands-on exercises using software packages implementing these algorithms (e.g., COPASI, COBRApy). The concepts are applied to the problem of optimal metabolite production for bacteria, the Warburg effect in mammalian (tumor) cells, metabolic regulation of multicellular organisms, and the dynamics of microbial ecosystems.

Course objectives

  • Learn to work with differential equation-based models and graph representation of (bio)chemical reaction networks, analysis methods and algorithms for computing flux balance analysis, elementary flux modes and extreme currents.

  • Understand the approaches and limitations of the modelling methods, read and understand current research on the topic.

  • Learn to approach biological questions on metabolism using differential equation-based modelling and flux-balance analysis.


The most recent timetable can be found on the students' website.

Mode of instruction

The class is taught primarily via lectures. Some lectures include step-by-step demonstrations of the software. Practical homework exercises ask students to use the presented software on new problems, and include also pen-and-paper exercises.
An individually written essay on a research paper will enable students to learn to read and interpret the literature hands-on.

Assessment Method

The final grade consists of two constituent examinations (30% + 30%), practical homework assignments (30%), and a practical team presentation (10%).

The constituent examinations are an individually written essay with subsequent oral presentation, and a written exam. The retakes for the essay is also written with subsequent oral presentation, and the written exam retake is written.
No minimum grade is required to take part for either exam. The homework and team presentation count as a practical and there is no retake for them.

To pass the course, the weighted average of all partial grades must be at least 5.5

Reading list

The course is based on materials collected from various sources and internal notes. Lecture slides will be made available for self-study. Various research papers will be distributed during the course.
Optional: Students may consult B.O. Palsson, Systems Biology: properties of reconstructed networks, Cambridge University Press, 2006 (ISBN 0-521-85903-4). It provides a broad view on the topic. It is not required.


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.


Lecturer: Dr. E. Tsingos
Email: e.tsingos[at]
Website: See the Brightspace page for the course