The statistical mechanics of complex signaling networks: nerve growth factor signaling
The statistical mechanics of complex signaling networks: nerve growth factor signaling
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DOI：
10.1088/1478-3967/1/3/006
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发表时间：
2004-09-01
期刊：
影响因子：
2
通讯作者：
Cerione, RA
中科院分区：
文献类型：
--
作者：
Brown, KS;Hill, CC;Cerione, RA
细胞信号网络的固有复杂性及其对广泛的细胞功能的重要性需要开发建模方法，这些方法可以用于进行预测和突出适当的实验，以测试我们对这些系统如何设计和功能的理解。我们使用统计力学的方法来提取有用的预测复杂的细胞信号网络。信号模型的一个关键困难是，虽然人们正在努力通过实验测量这些网络中各个步骤的速率常数，但描述其行为所需的许多参数仍然是未知的，或者充其量只是估计。为了建立我们的方法的有用性，我们已经应用我们的方法对神经生长因子（NGF）诱导的神经元细胞分化建模。特别地，我们研究了NGF和促有丝分裂表皮生长因子（EGF）在大鼠嗜铬细胞瘤（PC 12）细胞中的作用。通过中间信号蛋白的网络，这些生长因子中的每一种都以不同的动力学特征刺激细胞外调节激酶（Erk）磷酸化。使用我们的建模方法，我们能够预测特定信号传导模块在确定对两种生长因子的综合细胞反应中的影响。我们的方法也提出了一些有趣的见解的设计和可能的演变蜂窝系统，突出了这些系统的固有属性，我们称之为“草率”。
The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor ( NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor ( EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness'.