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Mathematical Biology, Metabolic Network Analysis


Admission requirements

Elementary calculus and linear algebra (and an interest in biology/biochemistry)


The course discusses the mathematical modelling of large biochemical networks, metabolic networks in particular, and the subsequent contrained-based analysis of their dynamic properties. Focus will be on the mathematical underpinning and algorithms involved. The necessary biological and biochemical background will be developed during the course. We introduce the fundamental concepts of the stoichiometric matrix and flux vector and show what information can already deduced from the first, e.g. concerning possible steady state flux vectors for the system: extreme currents, extreme pathways, elementary modes and the relationships among them. Several algorithms will be explained for computing them together with software packages that implement these (e.g. FluxAnalyzer). The concepts are applied to the problem of optimal metabolite production for a model organism. This is of importance in the production of e.g. pharmaceuticals in plant cell cultures or bacteria. If time permits, parametric sensitivity is discussed.

The course forms a good starting point for further specialisation in the master phase towards biomathematics.

Course objectives

The course aims to provide the students a concise overview of the state-of-the-art in stoichiometry-based metabolic network analysis, such that they are able to understand and discuss recent research papers in the field, at the end of the course. They know then the mathematical principles that underly the algorithms for computing the fundamental ‘elementary modes’ associated to a metabolic network and their attributes. They can perform these computations and apply the resulting modes in network analysis. That is, computing network statistics, interpret such statistics and compare them among different organisms. Moreover they can use these concepts in discussing the functioning of metabolic networks and changes therein due to genetic engineering. In the course they get accustomed to working in an interdisciplinary team, in these lectures with mathematicians and/or biologist.


The most recent timetable can be found at the LIACS website

Mode of instruction


Assessment method

Individual assignments and small-team projects with written report and presentation.
The final grade for the course is determined by weighted average of: (1) three take-home assignments (45%) , (2) an individually written essay on a research question covered by a collection (1 – 3) of recent research papers that apply the techniques discussed in the course (35%), which is also worked on in an interdisciplinary team, and (3) a team presentation on the topic of the essay (20%).

Reading list

Handouts of slides, partial lecture notes and research papers will be provided during the course. It is based on the book B.O. Palsson, Systems Biology: properties of reconstructed networks, Cambridge University Press, 2006 (ISBN 0-521-85903-4). Purchasing of the book may be helpful, but is not required.


You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.

Contact information

Study coordinator Computer Science, Riet Derogee


Mathematical Biology, Metabolic Network Analysis