Ruths Derek, Muller Melissa, Tseng Jen-Te, Nakhleh Luay, Ram Prahlad T
Department of Computer Science, Rice University, Houston, Texas, USA
PLoS Comput Biol. 2008 Feb 29;4(2):e1000005. doi: 10.1371/journal.pcbi.1000005.
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.
重建细胞信号网络并了解其工作原理是细胞生物学的主要研究方向。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对其进行分析极具挑战性。因此，人们开发了计算方法并将其与实验生物学方法相结合，产生了用于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频谱的两端。一端是一类结构网络分析方法；这些方法通常仅使用网络连接性来生成关于全局属性的假设。另一端是一类动态网络分析方法；除了连接性之外，这些方法还使用生化反应的动力学参数来预测网络的动态行为。这些预测为确定网络结构和行为方面的属性提供了详细的见解。然而，人们普遍认识到获取动力学参数数值的困难限制了后一类方法的适用性。几位研究人员观察到，仅网络的连接性就可以为其动态特性提供重要见解。受这一基本观察结果的启发，我们提出了信号Petri网，这是一种细胞信号网络的非参数模型，以及基于信号Petri网的模拟器，这是一种用于使用令牌分布和采样来表征信号通过信号网络流动动态的Petri网执行策略。结果是一种非常快速的方法，它可以分析大规模网络，并仅基于网络的连接性提供对分子活性水平响应外部刺激趋势的见解。我们已经在PathwayOracle工具包中实现了基于信号Petri网的模拟器，该工具包可在http://bioinfo.cs.rice.edu/pathwayoracle上公开获取。使用这种方法，我们研究了两种乳腺癌细胞系中EGFR下游的MAPK1,2和AKT信号网络。我们通过实验和计算分析了几种分子在对TSC2和mTOR-Raptor进行靶向操作时的活性水平。在超过90%的考虑案例中，我们方法的结果与实验结果一致，而在那些不一致的案例中，我们的方法为已知网络连接性与实验观察之间的差异提供了有价值的见解。
