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Bioinformatics Advance Access published online on January 5, 2008
Bioinformatics, doi:10.1093/bioinformatics/btm623
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Structural Systems Identification of Genetic Regulatory Networks

Hao Xiong 1,* and Yoonsuck Choe 2

1,2Department of Computer Science, Texas A&M University, College Station, TX 77843-3112.

*To whom correspondence should be addressed. Hao Xiong, E-mail: hxiong{at}cs.tamu.edu


  Abstract

Motivation: Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit.

Results: In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints.

Availability: The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

Contact: hxiong{at}cs.tamu.edu

Associate Editor: Dr. Olga Troyanskaya

Received on May 9, 2007; revised on November 14, 2007; accepted on December 14, 2007

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