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Open Access Methodology article

Using large-scale perturbations in gene network reconstruction

Thomas MacCarthy1*, Andrew Pomiankowski12 and Robert Seymour13

Author Affiliations

1 COMPLEX, University College London, 4 Stephenson Way, London NW1 2HE, UK

2 Department of Biology, University College London, 4 Stephenson Way, London NW1 2HE, UK

3 Department of Mathematics, University College London, Gower Street, London WC1E 2BT, UK

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BMC Bioinformatics 2005, 6:11  doi:10.1186/1471-2105-6-11

Published: 19 January 2005

Abstract

Background

Recent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computationally feasible. A simple enumerative reconstruction method based on a discrete dynamical system model is used to study how microarray experiments involving modulated global perturbations can be designed to obtain reasonably accurate reconstructions. The method is tested on artificial gene networks with biologically realistic in/out degree characteristics.

Results

It was found that a relatively small number of perturbations significantly improve inference accuracy, particularly for low-order inputs of one or two genes. The perturbations themselves should alter the expression level of approximately 50–60% of the genes in the network.

Conclusions

Time-series obtained from perturbations are a common form of expression data. This study illustrates how gene networks can be significantly reconstructed from such time-series while requiring only a relatively small number of calibrated perturbations, even for large networks, thus reducing experimental costs.