Computational modeling and analysis of the dynamics of control mechanisms in molecular interaction networks of neurons

最近在测量细胞现象方面的技术创新提供了收集细胞生物学系统定量建模所需数据的手段。如今，越来越多的生物学家采用系统生物学的方法，将计算建模和模拟与实验相结合。在这种情况下，建模可以成为生成可测试假设和促进理解的工具。在细胞中，生化信号很少通过简单、直接的途径传播，而是通过分子相互作用的网络传播。有了建模，就有可能理解这个网络的复杂动态。从理论的角度来看，要实现这种理解，需要更现实的模型和仿真数据的分析方法，突出系统属性。

The recent technical innovations in measurement of cellular phenomena provide the means to collect data required for quantitative modeling of cell biology systems. Nowadays, a growing number of biologists adopt a systems biology approach and integrate computational modeling and simulation with experiments in their scientific endeavor. In such setting, modeling can be a tool to generate testable hypotheses and to facilitate understanding. In the cell, biochemical signals are rarely propagated by simple, direct paths, but rather through a network of molecular interactions. With modeling, it is possible to understand the complex dynamics of this network. From a theoretical perspective, to achieve this understanding, one needs more realistic models and analysis methods of simulation data that highlight systemic properties.