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Bioinformatics Advance Access originally published online on August 19, 2004
Bioinformatics 2005 21(2):208-217; doi:10.1093/bioinformatics/bth479
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Bioinformatics vol. 21 issue 2 © Oxford University Press 2005; all rights reserved.

A method for estimating stochastic noise in large genetic regulatory networks

David Orrell *, Stephen Ramsey , Pedro de Atauri and Hamid Bolouri

Institute for Systems Biology 1441 North 34th Street, Seattle, WA 98103, USA

*To whom correspondence should be addressed.

Motivation: Genetic regulatory networks are often affected by stochastic noise, due to the low number of molecules taking part in certain reactions. The networks can be simulated using stochastic techniques that model each reaction as a stochastic event. As models become increasingly large and sophisticated, however, the solution time can become excessive; particularly if one wishes to determine the effect on noise of changes to a series of parameters, or the model structure. Methods are therefore required to rapidly estimate stochastic noise.

Results: This paper presents an algorithm, based on error growth techniques from non-linear dynamics, to rapidly estimate the noise characteristics of genetic networks of arbitrary size. The method can also be used to determine analytical solutions for simple sub-systems. It is demonstrated on a number of cases, including a prototype model of the galactose regulatory pathway in yeast.

Availability: A software tool which incorporates the algorithm is available for use as part of the stochastic simulation package Dizzy. It is available for download at http://labs.systemsbiology.net/bolouri/software/Dizzy/

Contact: dorrell{at}systemsbiology.org

Supplementary information: A conceptual model of the regulatory part of the galactose utilization pathway in yeast, used as an example in the paper, is available at http://labs.systemsbiology.net/bolouri/models/galconcept.dizzy


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