Impact of environmental inputs on reverse-engineering approach to network structures.

Impact of environmental inputs on reverse-engineering approach to network structures.
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DOI:
10.1186/1752-0509-3-113
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发表时间:
2009-12-04
影响因子:
--
通讯作者:
Feng J
Feng J
中科院分区:
生物2区
文献类型:
--
作者:
Wu J;Sinfield JL;Buchanan-Wollaston V;Feng J

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从生物系统中揭示复杂的网络结构是系统生物学的主要课题之一。网络结构可以通过动态贝叶斯网络或格兰杰因果关系来推断,但这两种技术都没有认真考虑环境投入的影响。考虑到生物数据的自然节律动力学,我们提出了一种系统生物学方法来揭示环境输入对网络结构的影响。我们首先用谐振子表示环境输入,并将它们与格兰杰因果关系相结合来识别环境输入,然后揭示因果网络结构。我们还将其推广到多个谐振子来表示各种外源影响。这个系统的方法是广泛的测试与玩具模型,并成功地应用到一个真实的生物网络的模式植物拟南芥开花基因的微阵列数据。其目的是确定那些直接受阳光影响的基因,并揭示与开花代谢相关的交互式网络结构。我们证明了环境输入是正确推断网络结构的关键。谐波因果分析方法被证明是一种检测环境输入和揭示网络结构的有效方法,特别是当生物数据呈现周期性振荡时。
Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.
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