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BMCBI
2007

A stochastic differential equation model for transcriptional regulatory networks

9 years 3 months ago
A stochastic differential equation model for transcriptional regulatory networks
Background: This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data sets. The dynamics of the gene expression level are fitted via a stochastic differential equation model, yielding a set of specific regulators and their contribution. Results: We show that a beta sigmoid function that keeps track of temporal parameters is a novel prototype of a regulatory function, with the effect of improving the performance of the profile prediction. The stochastic differential equation model follows well the dynamic of the gene expression levels. Conclusion: When adapted to biological hypotheses and combined with a promoter analysis, the method proposed here leads to improved models of the transcriptional regulatory networks. Background The production of independent sets of time courses of microarray data [1-3], obtained for the most studied eukaryotic organism Saccharomyces cerevisiae, impr...
Adriana Climescu-Haulica, Michelle D. Quirk
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Adriana Climescu-Haulica, Michelle D. Quirk
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