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Nucleic Acids Research Advance Access originally published online on December 14, 2006
Nucleic Acids Research 2007 35(1):279-287; doi:10.1093/nar/gkl1001
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Nucleic Acids Research, 2007, Vol. 35, No. 1 279-287
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae

Tra Thi Vu and Jiri Vohradsky*

Laboratory of Bioinformatics, Institute of Microbiology ASCR, Videnska 1083, 14220 Prague, Czech Republic

*To whom correspondence should be addressed. Tel: +420 241062513; Fax: +420 241722257; Email: vohr{at}biomed.cas.cz

Received June 19, 2006. Revised October 12, 2006. Accepted October 27, 2006.

Microarray studies are capable of providing data for temporal gene expression patterns of thousands of genes simultaneously, comprising rich but cryptic information about transcriptional control. However available methods are still not adequate in extraction of useful information about transcriptional regulation from these data. This study presents a dynamic model of gene expression which allows for identification of transcriptional regulators using time series of gene expression. The algorithm was applied for identification of transcriptional regulators controlling 40 cell cycle regulated genes of Saccharomyces cerevisiae. The presented algorithm uses a dynamic model of time continuous gene expression with the assumption that the target gene expression profile results from the action of the upstream regulator. The goal is to apply the model to putative regulators to estimate the transcription pattern of a target gene using a least squares minimization procedure. The procedure iteratively tests all possible transcription factors and selects those that best approximate the target gene expression profile. Results were compared with independently published data and good agreement between the published and identified transcriptional regulators was found.


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