TY - JOUR
T1 - Reconstructing dynamic gene regulatory networks from sample-based transcriptional data
AU - Zhu, Hailong
AU - Shyama Prasad Rao, R.
AU - Zeng, Tao
AU - Chen, Luonan
N1 - Funding Information:
Research Grants Council of Hong Kong [212111]; Startup Grant of Science Faculty of Hong Kong Baptist University [3840030]; NSFC [91029301, 61134013, 61072149 (in part)]; Chief Scientist Program of SIBS of CAS [2009CSP002 (in part)]; FIRST program from JSPS initiated by CSTP (in part). Funding for open access charge: Startup Grant of Science Faculty of Hong Kong Baptist University [3840030].
PY - 2012/11
Y1 - 2012/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84870578537&partnerID=8YFLogxK
U2 - 10.1093/nar/gks860
DO - 10.1093/nar/gks860
M3 - Journal article
C2 - 23002138
AN - SCOPUS:84870578537
SN - 0305-1048
VL - 40
SP - 10657
EP - 10667
JO - Nucleic Acids Research
JF - Nucleic Acids Research
IS - 21
ER -