Jashnsaz Hossein, Fox Zachary R, Hughes Jason J, Li Guoliang, Munsky Brian, Neuert Gregor
Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN 37232, USA.
Inria Saclay Ile-de-France, Palaiseau 91120, France.
iScience. 2020 Sep 15;23(10):101565. doi: 10.1016/j.isci.2020.101565. eCollection 2020 Oct 23.
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.
通过计算理解在不同环境、化学和基因扰动下引发细胞信号反应的分子机制，是一项长期存在的挑战，这需要能够拟合和预测新生物条件下定量反应的模型。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，用于使用多种不同的变化环境（以下简称“动力学刺激”）对通用信号网络进行扰动和建模，从而产生不同的信号通路激活动态。我们证明，利用多种不同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剂量反应或单个动力学刺激无法实现的信号动态。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动态，并量化哪些蛋白质和反应速率对哪些细胞外刺激最为敏感。
