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

Bickel DR, Montazeri Z, Hsieh PC, Beatty M, Lawit SJ, Bate NJ - Bioinformatics (2009)

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: 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

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).

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

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Posterior probabilities based on the average model [Equation (14)] for each of the four datasets. Black triangles represent genes encoding putative transcription factors and gray triangles represent the other probability-maximizing genes. The Supplementary Material reports the numeric values.
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Figure 3: Posterior probabilities based on the average model [Equation (14)] for each of the four datasets. Black triangles represent genes encoding putative transcription factors and gray triangles represent the other probability-maximizing genes. The Supplementary Material reports the numeric values.

Mentions: Table 1 summarizes the results for the four datasets. Figure 1, Figure 2 and Figure 3 show the probabilities of the genes that maximized the probability under the first-order, second-order and averaged models. The Supplementary Material supplies additional details about our analyses of these data.Fig. 1.


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

Bickel DR, Montazeri Z, Hsieh PC, Beatty M, Lawit SJ, Bate NJ - Bioinformatics (2009)

Posterior probabilities based on the average model [Equation (14)] for each of the four datasets. Black triangles represent genes encoding putative transcription factors and gray triangles represent the other probability-maximizing genes. The Supplementary Material reports the numeric values.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654806&req=5

Figure 3: Posterior probabilities based on the average model [Equation (14)] for each of the four datasets. Black triangles represent genes encoding putative transcription factors and gray triangles represent the other probability-maximizing genes. The Supplementary Material reports the numeric values.
Mentions: Table 1 summarizes the results for the four datasets. Figure 1, Figure 2 and Figure 3 show the probabilities of the genes that maximized the probability under the first-order, second-order and averaged models. The Supplementary Material supplies additional details about our analyses of these data.Fig. 1.

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: 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

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).

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

Show MeSH