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Open Access Research article

Visualizing regulatory interactions in metabolic networks

Stephan Noack1, Aljoscha Wahl2, Ermir Qeli3 and Wolfgang Wiechert4*

Author Affiliations

1 Institute of Biotechnology 2, Research Centre Jülich, Germany

2 MPI for Complex Technical Systems Magdeburg, Germany

3 Department of Mathematics and Computer Science, University of Marburg, Germany

4 Institute of Systems Engineering, Department of Simulation, University of Siegen, Germany

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BMC Biology 2007, 5:46  doi:10.1186/1741-7007-5-46

Published: 16 October 2007

Abstract

Background

Direct visualization of data sets in the context of biochemical network drawings is one of the most appealing approaches in the field of data evaluation within systems biology. One important type of information that is very helpful in interpreting and understanding metabolic networks has been overlooked so far. Here we focus on the representation of this type of information given by the strength of regulatory interactions between metabolite pools and reaction steps.

Results

The visualization of such interactions in a given metabolic network is based on a novel concept defining the regulatory strength (RS) of effectors regulating certain reaction steps. It is applicable to any mechanistic reaction kinetic formula. The RS values are measures for the strength of an up- or down-regulation of a reaction step compared with the completely non-inhibited or non-activated state, respectively. One numerical RS value is associated to any effector edge contained in the network. The RS is approximately interpretable on a percentage scale where 100% means the maximal possible inhibition or activation, respectively, and 0% means the absence of a regulatory interaction. If many effectors influence a certain reaction step, the respective percentages indicate the proportion in which the different effectors contribute to the total regulation of the reaction step. The benefits of the proposed method are demonstrated with a complex example system of a dynamic E. coli network.

Conclusion

The presented visualization approach is suitable for an intuitive interpretation of simulation data of metabolic networks under dynamic as well as steady-state conditions. Huge amounts of simulation data can be analyzed in a quick and comprehensive way. An extended time-resolved graphical network presentation provides a series of information about regulatory interaction within the biological system under investigation.