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Bioinformatics Advance Access published online on May 27, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn246
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Estimating dynamic models for gene regulation networks

Jiguo Cao 1 and Hongyu Zhao 2,*

1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A1S6, Canada (E-mail: jca76{at}sfu.ca).
2Department of Epidemiology and Public Health, Yale University, School of Medicine, 60 College Street, New Haven, CT 06520-8034, USA (E-mail: hongyu.zhao{at}yale.edu).

*To whom correspondence should be addressed. Hongyu Zhao, E-mail: hongyu.zhao{at}yale.edu


  Abstract

Motivation: Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data.

Results: In this article, we introduce the generalized profiling method to estimate ODE parameters in a gene regulation network from microarray gene expression data which can be rather noisy. Because numerically solving ODEs is computationally expensive, we apply the penalized smoothing technique, a fast and stable computational method to approximate ODE solutions. The ODE solutions with our parameter estimates fit the data well. A goodness-of-fit test of dynamic models is developed to identify gene regulation networks.

Contact: jca76{at}sfu.ca, hongyu.zhao{at}yale.edu

Associate Editor: Dr. Olga Troyanskaya

Received on April 25, 2008; revised on April 26, 2008; accepted on May 23, 2008

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[Abstract] [Full Text] [PDF]


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