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Open Access Highly Accessed Research article

Phenotype prediction in regulated metabolic networks

Christoph Kaleta12, Florian Centler124, Pietro Speroni di Fenizio123 and Peter Dittrich12*

Author Affiliations

1 Bio Systems Analysis Group, Department of Mathematics and Computer Science, Friedrich Schiller University Jena, Germany

2 Jena Centre for Bioinformatics, Jena, Germany

3 Research Institute in Networks & Communications Engineering (RINCE), Dublin City University, Ireland

4 Helmholtz Centre for Environmental Research – UFZ, Department of Environmental Microbiology, Leipzig, Germany

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BMC Systems Biology 2008, 2:37  doi:10.1186/1752-0509-2-37

Published: 25 April 2008

Abstract

Background

Due to the growing amount of biological knowledge that is incorporated into metabolic network models, their analysis has become more and more challenging. Here, we examine the capabilities of the recently introduced chemical organization theory (OT) to ease this task. Considering only network stoichiometry, the theory allows the prediction of all potentially persistent species sets and therewith rigorously relates the structure of a network to its potential dynamics. By this, the phenotypes implied by a metabolic network can be predicted without the need for explicit knowledge of the detailed reaction kinetics.

Results

We propose an approach to deal with regulation – and especially inhibitory interactions – in chemical organization theory. One advantage of this approach is that the metabolic network and its regulation are represented in an integrated way as one reaction network. To demonstrate the feasibility of this approach we examine a model by Covert and Palsson (J Biol Chem, 277(31), 2002) of the central metabolism of E. coli that incorporates the regulation of all involved genes. Our method correctly predicts the known growth phenotypes on 16 different substrates. Without specific assumptions, organization theory correctly predicts the lethality of knockout experiments in 101 out of 116 cases. Taking into account the same model specific assumptions as in the regulatory flux balance analysis (rFBA) by Covert and Palsson, the same performance is achieved (106 correctly predicted cases). Two model specific assumptions had to be considered: first, we have to assume that secreted molecules do not influence the regulatory system, and second, that metabolites with increasing concentrations indicate a lethal state.

Conclusion

The introduced approach to model a metabolic network and its regulation in an integrated way as one reaction network makes organization analysis a universal technique to study the potential behavior of biological network models. Applying multiple methods like OT and rFBA is shown to be valuable to uncover critical assumptions and helps to improve model coherence.