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一个包括转录后调控的扩展基因蛋白质/产物布尔网络模型。

An extended gene protein/products Boolean network model including post-transcriptional regulation.

作者信息

Benso Alfredo, Di Carlo Stefano, Politano Gianfranco, Savino Alessandro, Vasciaveo Alessandro

出版信息

Theor Biol Med Model. 2014 May 7;11 Suppl 1(Suppl 1):S5. doi: 10.1186/1742-4682-11-S1-S5.

Abstract

BACKGROUND

Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs.

RESULTS

In this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit.

CONCLUSIONS

The extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanisms.

摘要

背景

网络生物学允许使用形式化、结构良好且计算友好的模型来研究生物系统之间的复杂相互作用。根据需要研究的相互作用类型,可以创建几种不同的网络模型。基因调控网络(GRN)是一种常用的有效模型,用于研究细胞的复杂调控机制。不幸的是,由于其固有的复杂性和非离散性质,对实际大小的复杂GRN进行计算研究需要一些抽象。例如,布尔网络(BN)是一种可靠的模型,可用于表示节点的可能状态为布尔值(0或1)的网络。尽管有这种强烈的简化,BN已被用于研究真实的以及随机生成的GRN的结构和动态特性。

结果

在本文中,我们展示了如何将转录后调控机制(由微小非编码RNA分子如miRNA介导的关键过程)纳入GRN的BN模型。增强的BN模型在一个软件工具包(EBNT)中实现,该工具包允许从结构和动态角度分析布尔GRN。这个开源工具包与Cytoscape等可用的可视化工具兼容,并允许对网络拓扑及其吸引子、轨迹和状态空间进行详细分析。在本文中,围绕mTOR基因构建的一个小型GRN被用于展示该工具包的主要功能。

结论

本文提出的扩展模型为基因调控研究开辟了新的机会。在BN支持下进行的一些成功研究,旨在理解调控网络的高级特征,现在可以得到改进,以便更好地理解转录后调控的作用,例如作为一种全网络范围的降噪或稳定机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8159/4108923/00b5226c250f/1742-4682-11-S1-S5-1.jpg

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