APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.
APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.
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APG：一种活性蛋白质基因网络模型，用于量化复杂生物系统中的调节信号
DOI：
10.1038/srep01097
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发表时间：
2013
影响因子：
4.6
通讯作者：
Chen, Luonan
中科院分区：
文献类型：
--
作者：
Wang, Jiguang;Sun, Yidan;Zheng, Si;Zhang, Xiang-Sun;Zhou, Huarong;Chen, Luonan
转录因子及其辅因子之间的协同作用共同决定了复杂生物系统中的基因表达。在这项工作中，我们开发了一个新的图形模型，称为活性蛋白-基因(APG)网络模型，通过整合Tf上游调控和下游调控的高通量数据来量化复杂生物分子网络中的转录调控信号。首先，通过与仅基于Tf下游调节信息的传统策略进行比较，从理论和计算上论证了APG的有效性。然后，我们将该模型应用于自发性2型糖尿病大鼠Goto-Kakizaki(GK)和Wistar对照组大鼠。我们的生物实验验证了理论结果。特别是，SP1被发现是一种调节活性发生变化的隐藏转铁蛋白，而SP1活性的丧失是糖尿病发生过程中葡萄糖产生增加的原因之一。APG模型通过模拟转录因子的组合相互作用和利用多水平的高通量信息，为定量阐明转录调控提供了理论基础。
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.
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影响因子：
14.9
作者：
Kanehisa M;Goto S;Sato Y;Furumichi M;Tanabe M
通讯作者：
Tanabe M
影响因子：
8.2
作者：
Movassat, J;Saulnier, C;Portha, B
通讯作者：
Portha, B
影响因子：
7.7
作者：
Nielsen, JH;Galsgaard, ED;Carlsson, C
通讯作者：
Carlsson, C
影响因子：
56.9
作者：
Grimsby, J;Sarabu, R;Grippo, JF
通讯作者：
Grippo, JF
DOI：
10.1073/pnas.2136632100
发表时间：
2003-12-23
影响因子：
11.1
作者：
Liao, JC;Boscolo, R;Roychowdhury, VP
通讯作者：
Roychowdhury, VP