Zhang Fan, Chen Haoting, Zhao Li Na, Liu Hui, Przytycka Teresa M, Zheng Jie
Biomedical Informatics Graduate Lab, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA.
BMC Syst Biol. 2016 Jan 11;10 Suppl 1(Suppl 1):7. doi: 10.1186/s12918-015-0249-9.
Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.
We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.
The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.
细胞对细胞外扰动的反应需要信号通路来捕获和传递信号。然而，信号转导的潜在分子机制尚未完全了解，因此可能无法为所有信号通路提供详细和全面的模型。特别是，参数知识不足是定量动力学建模的长期障碍，这就需要使用无参数方法进行建模和模拟，以捕捉信号通路的动态特性。
我们提出了一个计算模型，该模型能够模拟对降解的分级反应、信号分子之间的S形生物学关系以及对细胞的预定扰动的影响。使用蛋白质磷酸化的实验数据对模拟结果进行了验证，表明所提出的模型能够捕捉信号转导过程中蛋白质活性的主要趋势。与现有模拟器相比，我们的模型在预测信号网络的状态转变方面具有更好的性能。
所提出的模拟工具为使用基于知识的方法对细胞信号通路进行建模提供了有价值的资源。
