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Bioinformatics Advance Access published online on March 31, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti415
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received February 4, 2005
Revised March 10, 2005
Accepted March 29, 2005

Article

A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae

Kuang-Chi Chen 1, Tse-Yi Wang 2, Huei-Hun Tseng 1, Chi-Ying F. Huang 3, and Cheng-Yan Kao 2*

1 Division of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan Town, Miaoli County, 350, Taiwan
2 Bioinformatic Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 106, Taiwan
3 Division of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan Town, Miaoli County, 350, Taiwan; Bioinformatic Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 106, Taiwan

* To whom correspondence should be addressed.
Cheng-Yan Kao, E-mail: cykao{at}csie.ntu.edu.tw


  Abstract

Motivation: The explosion of microarray studies has promised to shed light on the temporal expression patterns of thousands of genes simultaneously. However, available methods are far from adequate in efficiently extracting useful information to aid in a greater understanding of transcriptional regulatory network. Biological systems have been modeled as dynamic systems for a long history, such as genetic networks, cell regulatory network. This study evaluated whether the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process that originates from the irregular Brownian motion, can be applied in modeling the transcriptional regulatory network in Saccharomyces cerevisiae.

Results: To model the time-continuous gene expression datasets, a model of SDE is applied to depict irregular patterns. Our goal is to fit a generalized linear model by combining putative regulators to estimate the transcriptional pattern of a target gene. Goodness-of-fit is evaluated by log-likelihood and Akaike Information Criterion. Moreover, estimations of the contribution of regulators and inference of transcriptional pattern are implemented by statistical approaches. Our SDE model is basic but the test results agree well with the observed dynamic expression patterns. It implies that advanced SDE model might be perfectly suited to portray transcriptional regulatory networks.

Availability: The R code is available under request.

Supplementary information: http://www.csie.ntu.edu.tw/~b89x035/yeast/.


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