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Open Access Highly Accessed Research article

Reconstruction and logical modeling of glucose repression signaling pathways in Saccharomyces cerevisiae

Tobias S Christensen12, Ana Paula Oliveira13 and Jens Nielsen14*

Author Affiliations

1 Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark, Building 223, DK-2800 Kgs. Lyngby, Denmark

2 Current address: Department of Chemical Engineering, Massachusetts Institute of Technology, Building 66, 25 Ames Street, Cambridge, MA 02139, USA

3 Current address: Institute for Molecular Systems Biology, ETH Zurich, CH-8093, Zurich, Switzerland

4 Current address: Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden

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BMC Systems Biology 2009, 3:7  doi:10.1186/1752-0509-3-7

Published: 14 January 2009

Abstract

Background

In the yeast Saccharomyces cerevisiae, the presence of high levels of glucose leads to an array of down-regulatory effects known as glucose repression. This process is complex due to the presence of feedback loops and crosstalk between different pathways, complicating the use of intuitive approaches to analyze the system.

Results

We established a logical model of yeast glucose repression, formalized as a hypergraph. The model was constructed based on verified regulatory interactions and it includes 50 gene transcripts, 22 proteins, 5 metabolites and 118 hyperedges. We computed the logical steady states of all nodes in the network in order to simulate wildtype and deletion mutant responses to different sugar availabilities. Evaluation of the model predictive power was achieved by comparing changes in the logical state of gene nodes with transcriptome data. Overall, we observed 71% true predictions, and analyzed sources of errors and discrepancies for the remaining.

Conclusion

Though the binary nature of logical (Boolean) models entails inherent limitations, our model constitutes a primary tool for storing regulatory knowledge, searching for incoherencies in hypotheses and evaluating the effect of deleting regulatory elements involved in glucose repression.