Modeling stochasticity and robustness in gene regulatory networks.
Modeling stochasticity and robustness in gene regulatory networks.
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DOI：
10.1093/bioinformatics/btp214
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发表时间：
2009-06-15
期刊：
影响因子：
--
通讯作者：
Xenarios I
中科院分区：
文献类型：
--
作者：
Garg A;Mohanram K;Di Cara A;De Micheli G;Xenarios I
动机：了解生物过程中的基因调控和建模的鲁棒性的基础监管网络是一个重要的问题，目前正在解决的计算系统生物学家。最近，人们对基因调控网络（GRNs）的布尔建模技术重新产生了兴趣。然而，由于其确定性的性质，它往往是很难确定这些建模方法是否是强大的随机噪声，这是广泛的基因调控过程中。在过去，GRN的布尔模型中的随机性已经相对较少地解决，主要是通过以预定义的概率在不同表达水平之间翻转基因的表达。这种节点随机性（SIN）模型导致GRNs中噪声的过度表示，因此与生物学观察结果不一致。结果：在本文中，我们介绍了随机性函数（SIF）模型模拟随机性的布尔模型的GRNs。通过提供背后使用的SIF模型的生物学动机，并将其应用于T辅助细胞和T细胞激活网络，我们表明，SIF模型提供了更强大的生物学结果比现有的SIN模型的随机性GRNs。可用性：算法可在我们的布尔建模工具箱GenYsis中使用。软件二进制文件可从http：//si2.epfl.ch/genysis.html下载。联系人：abhishek. epfl.ch
Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. Results: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Availability: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/∼garg/genysis.html. Contact: abhishek.garg@epfl.ch
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