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Bioinformatics Advance Access published online on January 10, 2006
Bioinformatics, doi:10.1093/bioinformatics/btk034
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received November 30, 2005
Revised December 27, 2005
Accepted December 30, 2005

Article

Using a state-space model with hidden variables to infer transcription factor activities

Zheng Li 1, Stephen M. Shaw 1, Matthew J. Yedwabnick 1, and Christina Chan 1 *

1 Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, MI 48824

* To whom correspondence should be addressed.
Christina Chan, E-mail: krischan{at}egr.msu.edu


  Abstract

Motivation: In a gene regulatory network, genes are typically regulated by transcription factors (TF). The transcription factor activity (TFA) is more difficult to measure than are the gene expression levels. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. The hidden variables include regulatory motifs in the gene network, such as feed-back loops and auto-regulation, making SSM a useful complement to existing models.

Results: A gene regulatory network incorporating, for example, feed-forward loops, auto-regulation, and multiple inputs, was constructed with a SSM model. First, the gene expression data was simulated by SSM and used to infer the TFAs. The ability of SSM to infer TFAs was evaluated by comparing the profiles of the inferred and simulated TFA. Second, SSM was applied to gene expression data obtained from Escherichia coli K12 undergoing a carbon source transition and from the Saccharomyces cerevisiae cell cycle. The inferred activity profile for each TF was validated either by measurement or activity information from the literature. The SSM model provides a probabilistic framework to simulate gene regulatory networks and to infer activity profiles of hidden variables.

Availability: Supplementary data and Matlab code will be made available at the URL below.

Supplementary information: http://www.chems.msu.edu/groups/chan/ssm.zip.

Associate Editor: Steen Knudsen
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