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


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


BACKGROUND
http://www.davidbickel.com.
Bickel
David R
DR
Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, ON K1H 8M5, Canada. dbickel@uottawa.ca
Montazeri
Zahra
Z
Hsieh
Pei-Chun
PC
Beatty
Mary
M
Lawit
Shai J
SJ
Bate
Nicholas J
NJ
D001499:Bayes Theorem; D053263:Gene Regulatory Networks; D007700:Kinetics; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D014158:Transcription, Genetic