Zielinski Rafal, Przytycki Pawel F, Zheng Jie, Zhang David, Przytycka Teresa M, Capala Jacek
National Center for Biotechnology Information, National Library of Medicine National Institutes of Health Bethesda, MD, USA.
BMC Syst Biol. 2009 Sep 4;3:88. doi: 10.1186/1752-0509-3-88.
Cellular response to external stimuli requires propagation of corresponding signals through molecular signaling pathways. However, signaling pathways are not isolated information highways, but rather interact in a number of ways forming sophisticated signaling networks. Since defects in signaling pathways are associated with many serious diseases, understanding of the crosstalk between them is fundamental for designing molecularly targeted therapy. Unfortunately, we still lack technology that would allow high throughput detailed measurement of activity of individual signaling molecules and their interactions. This necessitates developing methods to prioritize selection of the molecules such that measuring their activity would be most informative for understanding the crosstalk. Furthermore, absence of the reaction coefficients necessary for detailed modeling of signal propagation raises the question whether simple parameter-free models could provide useful information about such pathways.
We study the combined signaling network of three major pro-survival signaling pathways: Epidermal Growth Factor Receptor (EGFR), Insulin-like Growth Factor-1 Receptor (IGF-1R), and Insulin Receptor (IR). Our study involves static analysis and dynamic modeling of this network, as well as an experimental verification of the model by measuring the response of selected signaling molecules to differential stimulation of EGF, IGF and insulin receptors. We introduced two novel measures of the importance of a node in the context of such crosstalk. Based on these measures several molecules, namely Erk1/2, Akt1, Jnk, p70S6K, were selected for monitoring in the network simulation and for experimental studies. Our simulation method relies on the Boolean network model combined with stochastic propagation of the signal. Most (although not all) trends suggested by the simulations have been confirmed by experiments.
The simple model implemented in this paper provides a valuable first step in modeling signaling networks. However, to obtain a fully predictive model, a more detailed knowledge regarding parameters of individual interactions might be necessary.
细胞对外界刺激的反应需要通过分子信号通路来传播相应的信号。然而，信号通路并非孤立的信息高速公路，而是以多种方式相互作用，形成复杂的信号网络。由于信号通路的缺陷与许多严重疾病相关，了解它们之间的相互作用对于设计分子靶向治疗至关重要。不幸的是，我们仍然缺乏能够对单个信号分子的活性及其相互作用进行高通量详细测量的技术。这就需要开发方法来优先选择分子，以便测量它们的活性对于理解相互作用最具信息价值。此外，信号传播详细建模所需的反应系数的缺失引发了一个问题，即简单的无参数模型是否能够提供有关此类通路的有用信息。
我们研究了三种主要的促生存信号通路的联合信号网络：表皮生长因子受体（EGFR）、胰岛素样生长因子-1受体（IGF-1R）和胰岛素受体（IR）。我们的研究包括对该网络的静态分析和动态建模，以及通过测量选定信号分子对EGF、IGF和胰岛素受体的差异刺激的反应来对模型进行实验验证。我们引入了两种在此类相互作用背景下衡量节点重要性的新方法。基于这些方法，选择了几个分子，即Erk1/2、Akt1、Jnk、p70S6K，用于网络模拟中的监测和实验研究。我们的模拟方法依赖于布尔网络模型与信号的随机传播相结合。模拟所暗示的大多数（尽管不是全部）趋势已通过实验得到证实。
本文中实现的简单模型为信号网络建模提供了有价值的第一步。然而，要获得一个完全可预测的模型，可能需要关于个体相互作用参数的更详细知识。
