Course content

This course will provide a 1-week intensive introduction to the computational analysis and reconstruction of both small and genome-scale metabolic networks. The goal is to make students familiar with state-of-the-art computational tools and databases, as well as providing the students with hands-on experience. Furthermore, we will cover the mathematical basis of constraint-based analysis of genome-scale metabolic models and provide a foundation for stability and control analysis of dynamic models. The first part of the course (2.5 days) will focus on the organization of metabolic networks; their reconstruction and the mathematical basis of optimization approaches used in constraint-based modeling. We will use the COBRA toolbox in Matlab and/or COBRApy for python. We will also discuss available methods for data integration. The second part of the course (2.5 days) will focus on the reconstruction and analysis of small, mechanistic models of metabolic pathways that allow the simulation of dynamic properties of the system. In this context, we will provide an introduction to stability and control analysis.

Learning outcome

– Explain the organization and structure of metabolic models – Knowledge about relevant databases – Capable of reconstructing metabolic models – Explain the underlying principles of constraint based modeling approaches – Explain stability and control analysis – Proficiency in use of COBRA toolbox for genome-scale metabolic modeling – Explain limits of small and genome-scale metabolic analysis – Knowledge about tools for data integration

Learning methods and activities

Lectures, presentation and computer exercises. The course will be given in English and will take place during 1 week of the semester.

Compulsory assignments

• Exercises

Specific conditions

Recommended previous knowledge

It is recommended that the participants have basic knowledge in bioinformatics, and mathematical knowledge of linear algebra and differential equations.

Course materials

Course materiel will be announced to registered participants before the start of the course.