Detecting robust time-delayed regulation in Mycobacterium tuberculosis

被引：5

作者：

Chaturvedi I. ^{[1
]}

Rajapakse J.C. ^{[1
,2
,3
]}

机构：

[1] Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University

[2] Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA

[3] Singapore-MIT Alliance

来源：

BMC Genomics | / 10卷 / Suppl 3期

关键词：

Mutual Information; Bayesian Network; Gene Regulatory Network; Transition Network; Viterbi Algorithm;

D O I：

10.1186/1471-2164-10-S3-S28

中图分类号：

学科分类号：

摘要：

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. © 2009 Chaturvedi and Rajapakse; licensee BioMed Central Ltd.

引用

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