The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.
The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.
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DOI：
10.1371/journal.pcbi.1000005
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发表时间：
2008-02-29
影响因子：
4.3
通讯作者：
Ram PT
中科院分区：
文献类型：
--
作者：
Ruths D;Muller M;Tseng JT;Nakhleh L;Ram PT
重建细胞信号网络并了解它们是如何工作的是细胞生物学的主要努力。然而，这些网络的规模和复杂性使得仅使用实验生物学方法进行分析非常具有挑战性。因此，计算方法被开发出来，并与实验生物学方法相结合，产生了分析这些网络的强大工具。这些计算方法大多落在模型参数化谱的两端。一端是一类结构化网络分析方法；这些方法通常仅使用网络连通性来生成有关全球属性的假设。另一方面是一类动态网络分析方法；这些方法除了使用连通性之外，还使用生化反应的动力学参数来预测网络的动态行为。这些预测提供了对决定网络结构和行为方面的属性的详细洞察。然而，人们普遍认为，获得动力学参数的数值的难度限制了后一类方法的适用性。几位研究人员观察到，仅网络的连通性就可以提供对其动态的重要洞察。在这一基本观察的基础上，我们提出了信令网和基于信令网的仿真器，信令网是一种非参数的细胞信令网络模型，基于信令网的模拟器是一种使用令牌分布和采样来描述信令网络中信令流动力学的执行策略。其结果是一种非常快速的方法，它可以分析大规模网络，并仅基于网络的连通性，提供对分子活动水平响应外部刺激的趋势的洞察。我们已经在Path Oracle工具包中实现了基于信令PETRI网的模拟器，该工具包可以在http://bioinfo.cs.rice.edu/pathwayoracle.上公开获得利用这种方法，我们研究了两个乳腺肿瘤细胞系中EGFR下游的MAPK1，2和AKT信号网络。我们从实验和计算两方面分析了几个分子对TSC2和mTOR-Raptor靶向操作的反应活性水平。在所考虑的90%以上的情况下，我们的方法的结果与实验结果一致，而在它们不一致的情况下，我们的方法为已知网络连通性与实验观察之间的差异提供了有价值的见解。许多细胞的行为，包括生长、分化和运动，都受到外界刺激的影响。这样的外部刺激通过信号网络--一个密集的细胞生化反应网络--获得、处理并传送到细胞核。除了有趣的是它们在指导细胞行为方面的作用外，细胞信号网络中的有害变化还可以改变细胞对外部刺激的反应，导致癌症等毁灭性疾病的发生。因此，建立准确的细胞信号网络的数学和计算模型是生物学中的一项主要工作。这些网络的规模和复杂性使得它们很难仅通过实验技术进行分析，这导致了计算分析方法的发展。在本文中，我们提出了一种新的计算模拟技术，它可以提供对蜂窝信令网络行为的定性准确的预测，而不需要详细了解信令网络的参数。我们的方法利用了最近的发现，即网络结构本身就可以决定网络动态的许多方面。与实验结果相比，我们的方法正确预测了90%的考虑案例。在那些它不同意的地方，我们的方法为已知网络结构和实验观察之间的差异提供了有价值的见解。
Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. Many cellular behaviors including growth, differentiation, and movement are influenced by external stimuli. Such external stimuli are obtained, processed, and carried to the nucleus by the signaling network—a dense network of cellular biochemical reactions. Beyond being interesting for their role in directing cellular behavior, deleterious changes in a cell's signaling network can alter a cell's responses to external stimuli, giving rise to devastating diseases such as cancer. As a result, building accurate mathematical and computational models of cellular signaling networks is a major endeavor in biology. The scale and complexity of these networks render them difficult to analyze by experimental techniques alone, which has led to the development of computational analysis methods. In this paper, we present a novel computational simulation technique that can provide qualitatively accurate predictions of the behavior of a cellular signaling network without requiring detailed knowledge of the signaling network's parameters. Our approach makes use of recent discoveries that network structure alone can determine many aspects of a network's dynamics. When compared against experimental results, our method correctly predicted 90% of the cases considered. In those where it did not agree, our approach provided valuable insights into discrepancies between known network structure and experimental observations.
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影响因子：
3.5
作者：
De Jong, H;Geiselmann, J;Page, M
通讯作者：
Page, M
影响因子：
2
作者：
GLASS, L;KAUFFMAN, SA
通讯作者：
KAUFFMAN, SA
影响因子：
64.5
作者：
Inoki, Ken;Ouyang, Hongjiao;Guan, Kun-Liang
通讯作者：
Guan, Kun-Liang
DOI：
10.1073/pnas.1536783100
发表时间：
2003-07-22
影响因子：
11.1
作者：
Aldana, M;Cluzel, P
通讯作者：
Cluzel, P
影响因子：
30.8
作者：
Belloni, E;Muenke, M;Scherer, SW
通讯作者：
Scherer, SW