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Open Access Highly Accessed Methodology article

A systematic approach to detecting transcription factors in response to environmental stresses

Li-Hsieh Lin1, Hsiao-Ching Lee2, Wen-Hsiung Li34 and Bor-Sen Chen1*

Author Affiliations

1 Lab of Systems Biology, Department of Electronical Engineering, National Tsing Hua University, 101, Sec 2, Kuang Fu Hsinchu, 300, Taiwan

2 Department of Life Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 300, Taiwan

3 Department of Ecology and Evolution, University of Chicago, USA

4 Genomics Research Center, Academia Sinica, Taipei, Taiwan

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BMC Bioinformatics 2007, 8:473  doi:10.1186/1471-2105-8-473

Published: 8 December 2007

Abstract

Background

Eukaryotic cells have developed mechanisms to respond to external environmental or physiological changes (stresses). In order to increase the activities of stress-protection functions in response to an environmental change, the internal cell mechanisms need to induce certain specific gene expression patterns and pathways by changing the expression levels of specific transcription factors (TFs). The conventional methods to find these specific TFs and their interactivities are slow and laborious. In this study, a novel efficient method is proposed to detect the TFs and their interactivities that regulate yeast genes that respond to any specific environment change.

Results

For each gene expressed in a specific environmental condition, a dynamic regulatory model is constructed in which the coefficients of the model represent the transcriptional activities and interactivities of the corresponding TFs. The proposed method requires only microarray data and information of all TFs that bind to the gene but it has superior resolution than the current methods. Our method not only can find stress-specific TFs but also can predict their regulatory strengths and interactivities. Moreover, TFs can be ranked, so that we can identify the major TFs to a stress. Similarly, it can rank the interactions between TFs and identify the major cooperative TF pairs. In addition, the cross-talks and interactivities among different stress-induced pathways are specified by the proposed scheme to gain much insight into protective mechanisms of yeast under different environmental stresses.

Conclusion

In this study, we find significant stress-specific and cell cycle-controlled TFs via constructing a transcriptional dynamic model to regulate the expression profiles of genes under different environmental conditions through microarray data. We have applied this TF activity and interactivity detection method to many stress conditions, including hyper- and hypo- osmotic shock, heat shock, hydrogen peroxide and cell cycle, because the available expression time profiles for these conditions are long enough. Especially, we find significant TFs and cooperative TFs responding to environmental changes. Our method may also be applicable to other stresses if the gene expression profiles have been examined for a sufficiently long time.