http://dx.doi.org/10.1093/nar/gks860">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Reconstructing dynamic gene regulatory networks from sample-based transcriptional data

Language

English

Abstract

The current method for reconstructing gene regulatory networks faces a dilemma concerning the study of bio-medical problems. On the one hand, static approaches assume that genes are expressed in a steady state and thus cannot exploit and describe the dynamic patterns of an evolving process. On the other hand, approaches that can describe the dynamical behaviours require time-course data, which are normally not available in many biomedical studies. To overcome the limitations of both the static and dynamic approaches, we propose a dynamic cascaded method (DCM) to reconstruct dynamic gene networks from samplebased transcriptional data. Our method is based on the intra-stage steady-rate assumption and the continuity assumption, which can properly characterize the dynamic and continuous nature of gene transcription in a biological process. Our simulation study showed that compared with static approaches, the DCM not only can reconstruct dynamical network but also can significantly improve network inference performance. We further applied our method to reconstruct the dynamic gene networks of hepatocellular carcinoma (HCC) progression. The derived HCC networks were verified by functional analysis and network enrichment analysis. Furthermore, it was shown that the modularity and network rewiring in the HCC networks can clearly characterize the dynamic patterns of HCC progression. © 2012 The Author(s).

Publication Date

2012

Source Publication Title

Nucleic Acids Research

Volume

40

Issue

21

Start Page

10657

End Page

10667

Publisher

Oxford University Press

ISSN (print)

03051048

ISSN (electronic)

13624962

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