Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.对细胞信号转导网络（cellular signaling networks）中的信号转导（signal transduction）过程进行模拟，可通过量化单个蛋白质的浓度与活性水平变化，实现对网络动态特性的预测。由于动力学参数的数值往往难以获取，因此亟需开发非参数（non-parametric）建模方法，将网络的连接结构与单个蛋白质对网络传导信号的响应特性相结合。现有非参数建模工具所计算得到的信号蛋白活性水平，与实验观测得到的数值之间未呈现显著相关性。本研究开发了一款非参数计算框架，用于描述信号转导网络中分子活性形式占比的演化过程特征与时间进程。该模型同样支持纳入扰动因素。通过在四个信号网络上开展验证实验，结果表明该模型可有效揭示信号转导过程中的活性水平与响应趋势。