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# Metabolic Network Analysis (BM)

Vak
2020-2021

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

## Description

The course considers the mathematical modelling of large biochemical networks, metabolic networks in particular, by means of various graph representations. Based on an appropriate graph representation, commonly used network statistics are discussed. Central is the use of the so-called stoichiometric matrix. It is fundamental in the subsequently discussed constrained-based analysis of the possible steady state fluxes through the network, Elementary (Flux) Mode Analysis in particular. Also the commonly used Flux Balance Analysis is presented. 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.

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

## Course objectives

The students get acquainted with common approaches to biochemical network modeling in terms of graphs, related concepts from network statistics and analysis approaches that are often encountered in the life science literature, in particular Flux Balance Analysis and Elementary Mode Analysis. They get to understand algorithms involved that compute relevant properties and are able to use software tools that have implemented these algorithms. It enables them to understand recent scientific literature in the Life Sciences that employ these techniques and critically assess the contents of papers.

## Timetable

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

## Mode of instruction

• Lectures (2 hours per week)

• 1-2 practical sessions

## Assessment

The final grade for the course is determined by weighted average of: (1) two take-home individual assignments (15 + 15%), (2) an individually written essay on a research question covered by one or two related research papers that apply or develop the techniques discussed in the course (30%), (3) an individual 15 minute presentation of the findings in the essay (10%), (4) a written exam on the theoretical topics discussed in the course (30%)

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.

## Registration

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

## Contact information

Lecturer: Dr. S. Hille