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Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network

Irons, D.J. and Monk, N.A.M. (2007) Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network. Bioinformatics, 8 (413). ISSN 1471-2105


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BACKGROUND It is widely accepted that genetic regulatory systems are 'modular', in that the whole system is made up of smaller 'subsystems' corresponding to specific biological functions. Most attempts to identify modules in genetic regulatory systems have relied on the topology of the underlying network. However, it is the temporal activity (dynamics) of genes and proteins that corresponds to biological functions, and hence it is dynamics that we focus on here for identifying subsystems.

RESULTS Using Boolean network models as an exemplar, we present a new technique to identify subsystems, based on their dynamical properties. The main part of the method depends only on the stable dynamics (attractors) of the system, thus requiring no prior knowledge of the underlying network. However, knowledge of the logical relationships between the network components can be used to describe how each subsystem is regulated. To demonstrate its applicability to genetic regulatory systems, we apply the method to a model of the Drosophila segment polarity network, providing a detailed breakdown of the system.

CONCLUSION We have designed a technique for decomposing any set of discrete-state, discrete-time attractors into subsystems. Having a suitable mathematical model also allows us to describe how each subsystem is regulated and how robust each subsystem is against perturbations. However, since the subsystems are found directly from the attractors, a mathematical model or underlying network topology is not necessarily required to identify them, potentially allowing the method to be applied directly to experimental expression data.

Item Type: Article
Copyright, Publisher and Additional Information: © 2007 Irons and Monk; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 31 Mar 2008 08:28
Last Modified: 08 Feb 2013 16:55
Published Version: http://dx.doi.org/10.1186/1471-2105-8-413
Status: Published
Publisher: Biomed Central
Refereed: Yes
Identification Number: 10.1186/1471-2105-8-413
URI: http://eprints.whiterose.ac.uk/id/eprint/3682

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