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Systems Biology

National facility

The immediate aim is to establish a competence center for computational systems biology, and in particular genome-scale modeling and simulation of biological and medical systems. A particular focus of the facility is the reconstruction and application of genome-scale metabolic models (GEMs), which represent large and well-documented biological networks that will be used for integrative analysis of high-throughput experimental data to a wide community of researchers.
In order to provide research service to the community, we will work in close collaboration with the other facilities at the Bioinformatics Platform. The Systems Biology facility will use already established routines for evaluation of project proposals together with the facility for Bioinformatics long-term support. For each supported project, a project team with relevant competence will be established with personnel resources from the different facilities at the Bioinformatics Platform.
The overriding goal of the facility is to provide a comprehensive framework and knowledge transfer environment that will enable the use of systems biology resources and methods for researchers across biological and medical disciplines.


- We provide a technology and competence resource for data integration and application of GEMs that can be used by a broad spectrum of biological and medical researchers
- Free of charge. Projects are reviewed by a national committee and selected based on scientific level.
- Long-term support. Senior bioinformaticians and computational biologists will be working in your project for a period of 6-12 months.


In addition to providing projects support, the facility will develop and maintain systems biology infrastructure resources such as a GEM database as well as a Reaction database. Please contact us for information on the current status and how to use these resources.


  • mapping of metabolic activities at pathway/reaction resolution for specific cell types
  • visualization of high-throughput experimental data in the context of metabolism
  • simulation of metabolic interactions between different human cell types and eventually perform whole body metabolism studies