Inferring Transcriptional Regulatory Networks from High-throughput Data.

@article{citeulike:1696070, abstract = {MOTIVATION: Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various posttranslational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes. RESULTS: In this paper, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming problem which has a globally optimal solution in terms ofL(1) norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes. AVAILABILITY: The software TRNinfer is available from http://intelligent.eic.osaka-sandai.ac.jp/chenen/TRNinfer.htm. CONTACT: chen@eic.osaka-sandai.ac.jp, zxs@amt.ac.cn.}, address = {School of Information, Renmin University of China, Beijing 100872, China, Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, Osaka 574-8530, Japan, Academy of Mathematics and Systems Science, CAS, Beijing 100080, China.}, author = {Wang, Rui-Sheng S. and Wang, Yong and Zhang, Xiang-Sun S. and Chen, Luonan }, citeulike-article-id = {1696070}, doi = {10.1093/bioinformatics/btm465}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {inference, network, regulation, transcription}, month = {September}, posted-at = {2007-11-21 15:35:54}, priority = {2}, title = {Inferring Transcriptional Regulatory Networks from High-throughput Data.}, url = {http://dx.doi.org/10.1093/bioinformatics/btm465}, year = {2007} }

TY - JOUR ID - citeulike:1696070 L3 - citeulike-article-id:1696070 TI - Inferring Transcriptional Regulatory Networks from High-throughput Data. JF - Bioinformatics AD - School of Information, Renmin University of China, Beijing 100872, China, Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, Osaka 574-8530, Japan, Academy of Mathematics and Systems Science, CAS, Beijing 100080, China. SN - 1460-2059 N2 - MOTIVATION: Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various posttranslational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes. RESULTS: In this paper, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming problem which has a globally optimal solution in terms ofL(1) norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes. AVAILABILITY: The software TRNinfer is available from http://intelligent.eic.osaka-sandai.ac.jp/chenen/TRNinfer.htm. CONTACT: chen@eic.osaka-sandai.ac.jp, zxs@amt.ac.cn. KW - inference KW - network KW - regulation KW - transcription AU - Wang, Rui-Sheng AU - Wang, Yong AU - Zhang, Xiang-Sun AU - Chen, Luonan PY - 2007/09/22/ UR - http://dx.doi.org/10.1093/bioinformatics/btm465 ER -