Purvis Jeremy E, Shih Andrew J, Liu Yingting, Radhakrishnan Ravi
Genomics and Computational Biology Graduate Group, University of Pennsylvania, 210 S. 33 Street, 240 Skirkanich Hall, Philadelphia PA, USA.
Department of Bioengineering, University of Pennsylvania, 210 S. 33 Street, 240 Skirkanich Hall, Philadelphia PA, USA.
Chapman Hall CRC Math Comput Biol Ser. 2011;2011:31-44.
A multiscale strategy is presented for constructing models of intracellular signaling networks in which the oncogenic behavior of the network is encoded through alternate parameterization of the kinetic and structural properties of mutant oncoproteins. The approach uses molecular dynamics and docking simulations to quantify altered topologies of interactions as well as to provide the missing parameters for network models of both wild-type and oncogenic signaling. Through simulation of the resulting signaling networks, the global behavior of these networks may then be compared and functional roles may be assigned to the mutant oncoproteins. An example of this approach is presented in which structural alterations found in a mutant form of the epidermal growth factor receptor are represented as kinetic perturbations in a model of growth factor signaling. Based on network parameters estimated from molecular-level simulations, simulations at the network level show that small perturbations in molecular structure can lead to profoundly altered cellular phenotype.
本文提出了一种多尺度策略，用于构建细胞内信号网络模型，其中网络的致癌行为通过突变癌蛋白的动力学和结构特性的交替参数化来编码。该方法使用分子动力学和对接模拟来量化相互作用拓扑结构的变化，并为野生型和致癌信号网络模型提供缺失的参数。通过对所得信号网络进行模拟，可以比较这些网络的全局行为，并为突变癌蛋白赋予功能作用。本文给出了一个该方法的示例，其中表皮生长因子受体突变形式中发现的结构改变在生长因子信号模型中被表示为动力学扰动。基于从分子水平模拟估计的网络参数，网络水平的模拟表明分子结构中的小扰动可导致细胞表型发生深刻改变。
