Department of Physics, The University of Texas at Austin, Austin, Texas 78712, USA.
J Chem Phys. 2010 Mar 28;132(12):121103. doi: 10.1063/1.3357980.
Cell signaling is fundamental to cell survival and disease progression. Traditional approaches to study these networks have focused largely on probabilistic approaches, with a large number of ad hoc assumptions. In this paper, we develop a linear Hamiltonian model to study the integrin signaling network. The integrin signaling network is central to cell adhesion, migration, and differentiation, but has not been studied in the same detail as other cell cycle networks. In this study, the integrin signaling network with 16 nodes in thermal fluctuations is analyzed through ensemble averages on the linear Hamiltonian model. This new and analytically rigorous approach offers a quick method to find out the dominant nodes in the complex network, which operate in the thermal noise regime. The robust on/off transitions due to the different initial inputs also reflect the inherent structure in the network, providing new insights into structure and function of the network.
细胞信号转导对于细胞存活和疾病进展至关重要。传统的研究这些网络的方法主要集中在概率方法上，并且有大量的特别假设。在本文中，我们开发了一种线性哈密顿模型来研究整合素信号网络。整合素信号网络是细胞黏附、迁移和分化的核心，但尚未像其他细胞周期网络那样进行详细研究。在这项研究中，通过在线性哈密顿模型上进行总体平均值分析，对具有 16 个节点的热波动的整合素信号网络进行了研究。这种新的、分析上严格的方法提供了一种快速方法来找出复杂网络中的主要节点，这些节点在热噪声环境下工作。由于不同的初始输入而产生的稳健的开/关转换也反映了网络中的固有结构，为网络的结构和功能提供了新的见解。
