Neves Susana R, Iyengar Ravi
Department of Pharmacology and Biological Chemistry, Mount Sinai School of Medicine, New York, NY 10029, USA.
Bioessays. 2002 Dec;24(12):1110-7. doi: 10.1002/bies.1154.
Biochemical networks, including those containing signaling pathways, display a wide range of regulatory properties. These include the ability to propagate information across different time scales and to function as switches and oscillators. The mechanisms underlying these complex behaviors involve many interacting components and cannot be understood by experiments alone. The development of computational models and the integration of these models with experiments provide valuable insight into these complex systems-level behaviors. Here we review current approaches to the development of computational models of biochemical networks and describe the insights gained from models that integrate experimental data, using three examples that deal with ultrasensitivity, flexible bistability and oscillatory behavior. These types of complex behavior from relatively simple networks highlight the necessity of using theoretical approaches in understanding higher order biological functions.
生化网络，包括那些含有信号通路的网络，展现出广泛的调控特性。这些特性包括在不同时间尺度上传播信息的能力，以及作为开关和振荡器发挥作用的能力。这些复杂行为背后的机制涉及许多相互作用的组件，仅凭实验无法理解。计算模型的开发以及这些模型与实验的整合，为深入了解这些复杂的系统层面行为提供了有价值的见解。在这里，我们回顾了当前生化网络计算模型开发的方法，并通过三个涉及超敏感性、灵活双稳态和振荡行为的例子，描述了从整合实验数据的模型中获得的见解。相对简单的网络所呈现的这些复杂行为类型，凸显了运用理论方法理解高阶生物学功能的必要性。
