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Bioinformatics Advance Access originally published online on February 13, 2009
Bioinformatics 2009 25(6):772-779; doi:10.1093/bioinformatics/btp028
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: a case for the second derivative

David R. Bickel 1,2,*, Zahra Montazeri 2, Pei-Chun Hsieh 3, Mary Beatty 4, Shai J. Lawit 4 and Nicholas J. Bate 4

1Ottawa Institute of Systems Biology, 2Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, ON K1H 8M5, Canada, 3Graduate Institute of Systems Biology and Bioinformatics, National Central University, No. 300, Jhongda Road, Jhongli City, Taoyuan County 32001, Taiwan (R.O.C.) and 4Pioneer Hi-Bred International, Inc., 7300 NW 62nd Avenue, PO Box 1004, Johnston, Iowa, IA 50131-1004, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made.

Results: The representation of a sufficiently large portion of genome enables computation of an upper bound on how much confidence one may place in influences between genes on the basis of expression data. Information about which genes encode transcription factors is not necessary but may be incorporated if available. The methodology is generalized to cover cases in which expression measurements are missing for many of the genes that might control the transcription of the genes of interest. The assumption that the gene expression level is roughly proportional to the rate of translation led to better empirical performance than did either the assumption that the gene expression level is roughly proportional to the protein level or the Bayesian model average of both assumptions.

Availability: http://www.oisb.ca points to R code implementing the methods (R Development Core Team 2004).

Contact: dbickel{at}uottawa.ca

Supplementary information: http://www.davidbickel.com

Associate Editor: Thomas Lengauer

Received on May 9, 2008; revised on December 4, 2008; accepted on January 12, 2009

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