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Dynamic cumulative activity of transcription factors as a mechanism of quantitative gene regulation

Feng He1, Jan Buer23, An-Ping Zeng45 and Rudi Balling1*

Author affiliations

1 Biological Systems Analysis Group, HZI- Helmholtz Centre for Infection Research, Inhoffenstrasse, D-38124 Braunschweig, Germany

2 Mucosal Immunity Group, HZI- Helmholtz Centre for Infection Research, Inhoffenstrasse, D-38124 Braunschweig, Germany

3 Institute of Medical Microbiology, Hannover Medical School (MHH), D-30625 Hannover, Germany

4 Systems Biology Group, HZI- Helmholtz Centre for Infection Research, Inhoffenstrasse, D-38124 Braunschweig, Germany

5 Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Denickerstrasse, D-21073 Hamburg, Germany

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Citation and License

Genome Biology 2007, 8:R181  doi:10.1186/gb-2007-8-9-r181

Published: 4 September 2007

Abstract

Background

The regulation of genes in multicellular organisms is generally achieved through the combinatorial activity of different transcription factors. However, the quantitative mechanisms of how a combination of transcription factors controls the expression of their target genes remain unknown.

Results

By using the information on the yeast transcription network and high-resolution time-series data, the combinatorial expression profiles of regulators that best correlate with the expression of their target genes are identified. We demonstrate that a number of factors, particularly time-shifts among the different regulators as well as conversion efficiencies of transcription factor mRNAs into functional binding regulators, play a key role in the quantification of target gene expression. By quantifying and integrating these factors, we have found a highly significant correlation between the combinatorial time-series expression profile of regulators and their target gene expression in 67.1% of the 161 known yeast three-regulator motifs and in 32.9% of 544 two-regulator motifs. For network motifs involved in the cell cycle, these percentages are much higher. Furthermore, the results have been verified with a high consistency in a second independent set of time-series data. Additional support comes from the finding that a high percentage of motifs again show a significant correlation in time-series data from stress-response studies.

Conclusion

Our data strongly support the concept that dynamic cumulative regulation is a major principle of quantitative transcriptional control. The proposed concept might also apply to other organisms and could be relevant for a wide range of biotechnological applications in which quantitative gene regulation plays a role.