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

Open Access Proceedings

Detecting robust time-delayed regulation in Mycobacterium tuberculosis

Iti Chaturvedi1 and Jagath C Rajapakse123

Author Affiliations

1 Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore, 639798

2 Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

3 Singapore-MIT Alliance, Singapore, 117543

BMC Genomics 2009, 10(Suppl 3):S28  doi:10.1186/1471-2164-10-S3-S28

Published: 3 December 2009

Abstract

Background

Time delays are often found in gene regulation though most techniques of building gene regulatory networks are not capable of capturing such phenomena. Here we look at the delays in the DNA repair system of Mycobacterium tuberculosis which is unusually slow in the bacteria. We propose a method based on a skip-chain model to study this phenomena in gene networks. The Viterbi paths of the underlying Markov chains find the most likely regulatory interactions among genes, taking care of very long delays. Using the derived networks, we discuss the delayed regulations and robustness of the DNA damage seen in the bacterium.

Results

We evaluated our method on time-course gene expressions after DNA damage with Mitocyin C. Several time-delayed interactions were observed with our analysis. The presence of hubs in the networks indicates that a small number of transcriptional factors regulate the rest of the system. We demonstrate the use of priors to overcome over-fitting problem in the generation of networks. We compare our results with the gene networks derived with dynamic Bayesian networks (DBN).

Conclusion

Different transcription networks are active at different stages, and constant feedback and regulation is maintained throughout the activities of a biological pathway. Skip-chain models are capable of capturing, long distant and the time-delayed regulations. Use of a Dirichlet prior over parameters and Gibbs prior over structure can greatly reduce the over-fitting in the new model.