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This article is part of the supplement: Eighth International Conference on Bioinformatics (InCoB2009): Computational Biology

Open Access Proceedings

Anomaly detection in gene expression via stochastic models of gene regulatory networks

Haseong Kim and Erol Gelenbe*

Author Affiliations

Intelligent Systems Networks Group, Electrical and Electronic Engineering Department, Imperial College London, UK

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BMC Genomics 2009, 10(Suppl 3):S26  doi:10.1186/1471-2164-10-S3-S26

Published: 3 December 2009

Abstract

Background

The steady-state behaviour of gene regulatory networks (GRNs) can provide crucial evidence for detecting disease-causing genes. However, monitoring the dynamics of GRNs is particularly difficult because biological data only reflects a snapshot of the dynamical behaviour of the living organism. Also most GRN data and methods are used to provide limited structural inferences.

Results

In this study, the theory of stochastic GRNs, derived from G-Networks, is applied to GRNs in order to monitor their steady-state behaviours. This approach is applied to a simulation dataset which is generated by using the stochastic gene expression model, and observe that the G-Network properly detects the abnormally expressed genes in the simulation study. In the analysis of real data concerning the cell cycle microarray of budding yeast, our approach finds that the steady-state probability of CLB2 is lower than that of other agents, while most of the genes have similar steady-state probabilities. These results lead to the conclusion that the key regulatory genes of the cell cycle can be expressed in the absence of CLB type cyclines, which was also the conclusion of the original microarray experiment study.

Conclusion

G-networks provide an efficient way to monitor steady-state of GRNs. Our method produces more reliable results then the conventional t-test in detecting differentially expressed genes. Also G-networks are successfully applied to the yeast GRNs. This study will be the base of further GRN dynamics studies cooperated with conventional GRN inference algorithms.