TY - JOUR
T1 - An integrative method to decode regulatory logics in gene transcription
AU - Yan, Bin
AU - Guan, Daogang
AU - Wang, Chao
AU - Wang, Junwen
AU - He, Bing
AU - Qin, Jing
AU - Boheler, Kenneth R.
AU - Lu, Aiping
AU - Zhang, Ge
AU - Zhu, Hailong
N1 - Funding Information:
This work was supported by the Research Grants Council of Hong Kong: GRF (grant No. 212111, 212613, and 17100214) and Theme-based Research Scheme T13-706/11; Interdisciplinary Research Matching Scheme of Hong Kong Baptist University RC-IRMS/12-13/02RC-IRMS/13-14/03 RC-IRMS/14-15/02; Strategic Development Fund of Hong Kong Baptist University SDF13-1209-P01; Faculty Research Grants of Hong Kong Baptist University FRG2/16-17/052; Seed Funding for Basic Research of the University of Hong Kong, RCGAS0768448734.
Publisher copyright:
© The Author(s) 2017
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF-TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.
AB - Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF-TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.
UR - http://www.scopus.com/inward/record.url?scp=85031918428&partnerID=8YFLogxK
U2 - 10.1038/s41467-017-01193-0
DO - 10.1038/s41467-017-01193-0
M3 - Journal article
C2 - 29051499
AN - SCOPUS:85031918428
SN - 2041-1723
VL - 8
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 1044
ER -
