受体交互作用的随机网络模型预测抗血管生成作用
Stochastic Network Model of Receptor Cross-Talk Predicts Anti-Angiogenic Effects
摘要：
肿瘤的侵袭和转移依赖于血管生成。在血管生成过程中发生的细胞过程（生长、迁移和凋亡）受到信号分子的严格调控。因此，了解细胞如何合成由关键外部刺激引发的多种生化信号可以导致开发新的治疗策略来对抗癌症。面对大量实验室使用各种检测方法产生的大量不相交的实验数据，理论信号转导模型提供了一个框架来提取大量数据。这种模型提供了一个机会，制定和测试新的假设，并可用于作出实验验证的预测。这项研究是第一个提出一个网络模型，突出了参与血管生成的关键受体，即生长因子，整合素和钙粘蛋白受体之间的串扰。从已有的实验数据出发，构建了受体串扰的随机布尔网络模型，系统地分析了该网络在带噪声生产函数的连续时间布尔动力学下的动力学稳定性.我们发现，信号转导网络表现出强大的和快速的响应外部信号，独立于内部细胞状态。我们推导出一个输入输出表，将外部刺激映射到细胞表型，该表对分子噪声非常稳定，但有一个重要的例外：关键信号分子RhoA和Rac1之间的振荡反馈回路在任意低噪声下不稳定，导致不稳定，功能失调的细胞运动。最后，我们发现该网络表现出随噪声增加的凋亡响应率，这表明程序性细胞死亡的概率取决于细胞健康。
英文摘要：
Cancer invasion and metastasis depend on angiogenesis. The cellular processes (growth, migration, and apoptosis) that occur during angiogenesis are tightly regulated by signaling molecules. Thus, understanding how cells synthesize multiple biochemical signals initiated by key external stimuli can lead to the development of novel therapeutic strategies to combat cancer. In the face of large amounts of disjoint experimental data generated from multitudes of laboratories using various assays, theoretical signal transduction models provide a framework to distill this vast amount of data. Such models offer an opportunity to formulate and test new hypotheses, and can be used to make experimentally verifiable predictions. This study is the first to propose a network model that highlights the cross-talk between the key receptors involved in angiogenesis, namely growth factor, integrin, and cadherin receptors. From available experimental data, we construct a stochastic Boolean network model of receptor cross-talk, and systematically analyze the dynamical stability of the network under continuous-time Boolean dynamics with a noisy production function. We find that the signal transduction network exhibits a robust and fast response to external signals, independent of the internal cell state. We derive an input-output table that maps external stimuli to cell phenotypes, which is extraordinarily stable against molecular noise with one important exception: an oscillatory feedback loop between the key signaling molecules RhoA and Rac1 is unstable under arbitrarily low noise, leading to erratic, dysfunctional cell motion. Finally, we show that the network exhibits an apoptotic response rate that increases with noise, suggesting that the probability of programmed cell death depends on cell health.
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