Reconstructing dynamic gene regulatory networks from sample-based transcriptional data

Hailong ZHU, R. Shyama Prasad Rao, Tao Zeng*, Luonan Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)10657-10667
Number of pages11
JournalNucleic Acids Research
Volume40
Issue number21
DOIs
Publication statusPublished - Nov 2012

Scopus Subject Areas

  • Genetics

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