TY - JOUR
T1 - Inferring the regulatory interaction models of transcription factors in transcriptional regulatory networks
AU - Awad, Sherine
AU - Panchy, Nicholas
AU - Ng, See Kiong
AU - Chen, Jin
PY - 2012/10
Y1 - 2012/10
N2 - Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TFtarget interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TFtarget interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TFtarget interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TFtarget regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.
AB - Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TFtarget interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TFtarget interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TFtarget interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TFtarget regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.
KW - Transcriptional regulation
KW - hidden Markov model
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UR - http://www.scopus.com/inward/citedby.url?scp=84864425734&partnerID=8YFLogxK
U2 - 10.1142/S0219720012500126
DO - 10.1142/S0219720012500126
M3 - Article
C2 - 22849367
AN - SCOPUS:84864425734
SN - 0219-7200
VL - 10
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
IS - 5
M1 - 1250012
ER -