文章基本信息

标题：APG: an Active Protein-Gene Network Model to Quantify Regulatory Signals in Complex Biological Systems
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作者：Jiguang Wang ; Yidan Sun ; Si Zheng 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2013
卷号：3
期号：1
DOI：10.1038/srep01097
语种：English
出版社：Springer Nature
摘要：Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.