Computationally understanding the molecular mechanisms that give rise to cell signaling responses upon different environmental, chemical, and genetic perturbations is a long-standing challenge that requires models that fit and predict quantitative responses for new biological conditions. Overcoming this challenge depends not only on good models and detailed experimental data but also on the rigorous integration of both. We propose a quantitative framework to perturb and model generic signaling networks using multiple and diverse changing environments (hereafter “kinetic stimulations”) resulting in distinct pathway activation dynamics. We demonstrate that utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional dose-response or individual kinetic stimulations. To demonstrate our approach, we use experimentally identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.

通过计算来理解在不同的环境，化学和遗传干扰下会引起细胞信号传导反应的分子机制是一项长期存在的挑战，需要建立适合新生物条件并预测其定量反应的模型。克服这一挑战不仅取决于良好的模型和详细的实验数据，还取决于两者的严格整合。我们提出了一个定量框架，以使用多种多样的变化环境（以下称为“运动刺激”）对通用信号网络进行干扰和建模，从而产生独特的途径激活动力学。我们证明利用多种多样的动力学刺激可以更好地约束模型参数，并能够预测使用传统的剂量反应或单独的动力学刺激不可能实现的信号动力学。为了证明我们的方法，我们使用实验确定的模型来预测在多种动力学刺激下正常，突变和药物处理条件下的信号传导动态，并量化哪种蛋白质和反应速率对哪种细胞外刺激最敏感。