Qiao Liang, Nachbar Robert B, Kevrekidis Ioannis G, Shvartsman Stanislav Y
Department of Chemical Engineering, Princeton University, Princeton, New Jersey, USA.
PLoS Comput Biol. 2007 Sep;3(9):1819-26. doi: 10.1371/journal.pcbi.0030184. Epub 2007 Aug 6.
Physicochemical models of signaling pathways are characterized by high levels of structural and parametric uncertainty, reflecting both incomplete knowledge about signal transduction and the intrinsic variability of cellular processes. As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. At this level of uncertainty, model analysis should emphasize statistics of systems-level properties, rather than the detailed structure of solutions or boundaries separating different dynamic regimes. Based on the combination of random parameter search and continuation algorithms, we developed a methodology for the statistical analysis of mechanistic signaling models. In applying it to the well-studied MAPK cascade model, we discovered a large region of oscillations and explained their emergence from single-stage bistability. The surprising abundance of strongly nonlinear (oscillatory and bistable) input/output maps revealed by our analysis may be one of the reasons why the MAPK cascade in vivo is embedded in more complex regulatory structures. We argue that this type of analysis should accompany nonlinear multiparameter studies of stationary as well as transient features in network dynamics.
信号通路的物理化学模型具有高度的结构和参数不确定性，这既反映了对信号转导的不完全了解，也反映了细胞过程的内在变异性。因此，这些模型试图预测具有数十甚至数百个自由参数的系统的动态变化。在这种不确定性水平下，模型分析应强调系统级属性的统计，而不是解的详细结构或分隔不同动态区域的边界。基于随机参数搜索和延拓算法的结合，我们开发了一种用于机械信号模型统计分析的方法。将其应用于经过充分研究的MAPK级联模型时，我们发现了一个很大的振荡区域，并解释了它们从单级双稳性中出现的原因。我们的分析揭示的强非线性（振荡和双稳）输入/输出映射的惊人丰富性，可能是体内MAPK级联嵌入更复杂调节结构的原因之一。我们认为，这种类型的分析应该伴随网络动力学中稳态和瞬态特征的非线性多参数研究。
