Reconstruction and logical modeling of glucose repression signaling pathways in Saccharomyces cerevisiae
Journal article, 2009

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.

mal gene-expression

transcriptional regulation

transporter genes

metabolic

transduction pathways

functional-analysis

biological networks

protein-phosphorylation sites

yeast

models

regulatory networks

Author

T. S. Christensen

Massachusetts Institute of Technology (MIT)

Technical University of Denmark (DTU)

A. P. Oliveira

Swiss Federal Institute of Technology in Zürich (ETH)

Technical University of Denmark (DTU)

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences

BMC Systems Biology

1752-0509 (eISSN)

Vol. 3 7 (artno)- 7

Subject Categories

Industrial Biotechnology

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1186/1752-0509-3-7

More information

Latest update

4/11/2018