Discription
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.
Lecture hours
2 per week
Assessment
The final grade for the course is determined by weighted average of: (1) three take-home individual 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%).
Literature
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.
Prerequisites
Elementary calculus and linear algebra (and an interest in biology/biochemistry)
Remarks
For all material and up to date information about the course see the lecturer’s home page