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This article is part of the supplement: Articles selected from posters presented at the Tenth Annual International Conference on Research in Computational Biology

Open Access Research

A stochastic differential equation model for transcriptional regulatory networks

Adriana Climescu-Haulica1* and Michelle D Quirk2

Author Affiliations

1 Laboratoire Biologie, Informatique, Mathématiques, Institute de Recherche en Technologies et Sciences pour le Vivant CEA, 17 Rue des Martyrs, Grenoble, 38052 France

2 Decision Applications Division, Los Alamos National Laboratory, MS K551, Los Alamos, New Mexico, 87545 USA

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BMC Bioinformatics 2007, 8(Suppl 5):S4  doi:10.1186/1471-2105-8-S5-S4

Published: 24 May 2007

Abstract

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.