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Bioinformatics Advance Access published online on April 8, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth236
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received October 28, 2003
Revised March 5, 2004
Accepted March 20, 2004

Article

Probabilistic representation of gene regulatory networks

Linyong Mao 1 Haluk Resat 1*

1 Computational Biosciences Group, Pacific Northwest National Laboratory, P.O. Box 999, MS: K1-92, Richland, WA 99352

* To whom correspondence should be addressed. E-mail: haluk.resat{at}pnl.gov.


  Abstract

Motivation: Recent experiments have unambiguously established that biological systems can have significant cell to cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation.

Results: In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library recently bioengineered by Guet et al. (Science, 296, 1466, 2002). The good agreement between the computed and experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data.

Availability: The simulation software is available upon request.

Supplementary Information: Supplementary material will be made available on the OUP server.


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