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

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 regu...

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Bibliographic Details
Published in:Bioinformatics 2005-06, Vol.21 (12), p.2883-2890
Main Authors: Chen, Kuang-Chi, Wang, Tse-Yi, Tseng, Huei-Hun, Huang, Chi-Ying F., Kao, Cheng-Yan
Format: Article
Language:English
Subjects:
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
Gene Expression Regulation - physiology
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Biological
Models, Statistical
Saccharomyces cerevisiae
Saccharomyces cerevisiae - physiology
Saccharomyces cerevisiae Proteins - metabolism
Signal Transduction - physiology
Software
Stochastic Processes
Transcription Factors - metabolism
Transcriptional Activation - physiology
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Summary: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 and cell regulatory network. This study evaluated if the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process originating 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 on request. Contact: cykao@csie.ntu.edu.tw Supplementary information: http://www.csie.ntu.edu.tw/~b89x035/yeast/
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bti415