Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data.
Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data.
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DOI：
10.1371/journal.pone.0006522
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发表时间：
2009-08-26
期刊：
影响因子：
3.7
通讯作者：
Wolf-Yadlin A
中科院分区：
文献类型：
--
作者：
Locasale JW;Wolf-Yadlin A
随着信号在不同条件下响应外部刺激而通过复杂网络传播，质谱分析等技术的进步使得能够对磷酸化水平进行定量、可重复的全蛋白质组测量。然而，严格从蛋白质组数据的定量方面推断信号网络元素的计算方法尚未建立。我们考虑了一种使用最大熵原理的方法，从质谱数据集中的成对相关性推断相互作用的磷酸酪氨酸位点网络，并仅从定量蛋白质组数据导出磷酸化依赖性相互作用网络。我们首先通过模拟生化信号网络模型来研究该方法的适用性，该网络的动力学由大量耦合微分方程控制。我们发现，在模拟信号系统中，该方法可以非常准确地检测相互作用。然后，我们分析了人类乳腺上皮细胞系中生长因子介导的信号网络，并观察了信号节点的生物学可解释的小世界结构，以及关于先前未表征的磷酸酪氨酸位点之间相互作用的预测目录。例如，计算将最近发现的通过 ARHGEF7 和 Scribble 的肿瘤抑制途径置于生长因子信号传导的背景下。我们的研究结果表明，最大熵衍生网络模型是解释定量蛋白质组数据的重要工具。
Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemical signaling network whose dynamics are governed by a large set of coupled differential equations. We found that in a simulated signaling system, the method detects interactions with significant accuracy. We then analyzed a growth factor mediated signaling network in a human mammary epithelial cell line that we inferred from mass spectrometry data and observe a biologically interpretable, small-world structure of signaling nodes, as well as a catalog of predictions regarding the interactions among previously uncharacterized phosphotyrosine sites. For example, the calculation places a recently identified tumor suppressor pathway through ARHGEF7 and Scribble, in the context of growth factor signaling. Our findings suggest that maximum entropy derived network models are an important tool for interpreting quantitative proteomics data.
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影响因子：
2.1
作者：
Maeda, Akio;Ozaki, Yu-ichi;Kuroda, Shinya
通讯作者：
Kuroda, Shinya
DOI：
10.1073/pnas.1633513100
发表时间：
2003-08-05
影响因子：
11.1
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DOI：
10.1073/pnas.0609152103
发表时间：
2006-12-12
影响因子：
11.1
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通讯作者：
Fedoroff, Nina V.
影响因子：
64.8
作者：
Schneidman, E;Berry, MJ;Bialek, W
通讯作者：
Bialek, W