Forskerkurs: Bringing genomes to life - Reconstruction and application of genome-scale networks

This course aims to provide hands-on experience to novices in computational systems biology by combining experimental data and mathematical modeling with emphasis on kinetic modeling of cellular pathways.

Tid og sted


Reykjavik, Island 20.06.2011 til 24.06.2011



+354 6186246

The course focuses on bacteria as model systems as these are amenable to simple studies to verify predicted models. Furthermore, possible biomedical and biotechnological applications are introduced. The course will be taught by world experts in the field of Systems Biology.

Metabolic reconstructions are a common denominator in systems biology. They represent biochemically, genetically and genomically structured knowledge-bases that capture current knowledge about an organism. To-date, metabolic networks have been reconstructed for > 50 organisms, based on well-defined procedures. Laboratories around the world are reconstructing metabolic networks for their organisms of interest, thus, there is an increasing need of researchers being familiar with the reconstruction process.

These metabolic networks can be readily converted into mathematical models and then used to investigate the genotype-phenotype relationship. Metabolic models have been successfully applied in at least 6 scientific areas: metabolic engineering, model-directed discovery, interpretations of phenotypic screens, network property analysis, drug-target identification, and studies of evolutionary processes.
The course will teach the reconstruction process and different modeling techniques employed in the aforementioned areas. Recently, approaches have been developed to investigate dynamics states of genome-scale metabolic networks. Basics methods are introduced in the course.


The course targets PhD students, post-docs, university faculty and life-science researchers from various scientific backgrounds. In particular, the course is oriented towards students interested in interdisciplinary research spanning biology, mathematics, and computer science. Knowledge in these three areas is not required since the course teaches the necessary background and basic principles.

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