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Sig2GRN：一种用于动态模拟的将信号通路与基因调控网络相连接的软件工具。

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

作者信息

Zhang Fan, Liu Runsheng, Zheng Jie

机构信息

School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.

Complexity Institute, Nanyang Technological University, Singapore, 637723, Singapore.

出版信息

BMC Syst Biol. 2016 Dec 23;10(Suppl 4):123. doi: 10.1186/s12918-016-0365-1.

DOI:10.1186/s12918-016-0365-1
PMID:28155685
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5259907/
Abstract

BACKGROUND

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways.

METHODS

A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways.

RESULTS

Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments.

CONCLUSIONS

As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design.

AVAILABILITY

http://histone.scse.ntu.edu.sg/Sig2GRN/.

摘要

背景

将信号通路的计算模型与预测的细胞反应（如基因表达调控）相联系是计算系统生物学中的一项重大挑战。在这项工作中，我们展示了Sig2GRN，这是一个Cytoscape插件，能够在给定用户定义的信号通路外部刺激的情况下模拟时间进程基因表达数据。

方法

使用广义逻辑模型对上游信号通路进行建模。然后采用布尔模型和基于热力学的模型，根据信号通路中转录因子的模拟动态来预测基因表达的下游变化。

结果

我们的实证案例研究表明，Sig2GRN的模拟可以预测由DNA损伤信号和药物处理诱导的基因表达模式变化。

结论

作为一种用于模拟细胞动态的软件工具，Sig2GRN可以通过假设生成和湿实验室实验设计促进系统生物学研究。

可用性

http://histone.scse.ntu.edu.sg/Sig2GRN/

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/8d1001d59ea7/12918_2016_365_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/35265b3d4810/12918_2016_365_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/81654091807f/12918_2016_365_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/c7bfc6f4a37d/12918_2016_365_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/b7d8836c8494/12918_2016_365_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/8d1001d59ea7/12918_2016_365_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/35265b3d4810/12918_2016_365_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/81654091807f/12918_2016_365_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/c7bfc6f4a37d/12918_2016_365_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/b7d8836c8494/12918_2016_365_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcf5/5259907/8d1001d59ea7/12918_2016_365_Fig5_HTML.jpg

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