Bioinformatics Advance Access originally published online on January 31, 2007
Bioinformatics 2007 23(7):866-874; doi:10.1093/bioinformatics/btm021
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Boolean dynamics of genetic regulatory networks inferred from microarray time series data
1Sandia National Laboratories, Computational Biology Department PO Box 5800, Albuquerque, NM, 87185-1316, USA, 2Sandia National Laboratories, Biosystems Research, PO Box 969, Livermore, CA 94551-9291, USA 3Sandia National Laboratories, Biomolecular Analysis and Imaging, PO Box 5800, Albuquerque, NM 87185-0895, USA and 4Sandia National Laboratories, Computational Biosciences Department PO Box 5800, Albuquerque, NM 87185-1413, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction.
Results: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics.
Contact: jfaulon{at}sandia.gov
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on September 14, 2006; revised on December 29, 2006; accepted on January 19, 2007
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