Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health , Bethesda, MD 20892, USA.
Sci Rep. 2012;2:616. doi: 10.1038/srep00616. Epub 2012 Aug 31.
Mathematical modeling can provide unique insights and predictions about a signaling pathway. Parameter variations allow identification of key reactions that govern signaling features such as the response time that may have a direct impact on the functional outcome. The effect of varying one parameter, however, may depend on values of another. To address the issue, we performed multi-parameter variations of an experimentally validated mathematical model of NF-κB regulatory network, and analyzed the inter-relationships of the parameters in shaping key dynamic features. We find that nonlinear dependencies are ubiquitous among parameters. Such phenomena may underlie the emergence of cell type-specific behaviors from essentially the same molecular network. Our results from a multivariate ensemble of models highlight the hypothesis that cell type specificity in signaling phenotype can arise from quantitatively altered strength of reactions in the pathway, in the absence of tissue-specific factors that re-wire the network for a new topology.
数学建模可以为信号通路提供独特的见解和预测。参数变化可以识别控制信号特征的关键反应，例如响应时间，这些特征可能直接影响功能结果。然而，一个参数的变化效果可能取决于另一个参数的值。为了解决这个问题，我们对 NF-κB 调控网络的一个经过实验验证的数学模型进行了多参数变化，并分析了参数之间的相互关系，以塑造关键动态特征。我们发现参数之间存在普遍的非线性关系。这种现象可能是从本质上相同的分子网络中产生细胞类型特异性行为的基础。我们从模型的多元集合中得到的结果强调了这样一个假设，即信号表型中的细胞类型特异性可以源自通路中反应强度的定量改变，而不存在重新为新拓扑布线的组织特异性因素。
