Wuensche A
Santa Fe Institute, New Mexico 87501, USA.
Pac Symp Biocomput. 1998:89-102.
Many natural processes consist of networks of interacting elements which affect each other's state over time, the dynamics depending on the pattern of connections and the updating rules for each element. Genomic regulatory networks are arguably networks of this sort. An attempt to understand genomic networks would benefit from the context of a general theory of discrete dynamical networks which is currently emerging. A key notion here is global dynamics, whereby state-space is organized into basins of attraction, objects that have only recently become accessible by computer simulation of idealized models, in particular "random Boolean networks". Cell types have been explained as attractors in genomic networks, where the network architecture is biased to achieve a balance between stability and adaptability in response to perturbation. Based on computer simulations using the software Discrete Dynamics Lab (DDLab), these ideas are described, as well as order-chaos measures on typical trajectories that further characterize network dynamics.
许多自然过程由相互作用的元素网络组成，这些元素随时间相互影响彼此的状态，其动态变化取决于连接模式和每个元素的更新规则。基因组调控网络可以说是这类网络。试图理解基因组网络将受益于目前正在兴起的离散动态网络一般理论的背景。这里的一个关键概念是全局动态，即状态空间被组织成吸引子盆地，这些对象直到最近才通过理想化模型（特别是“随机布尔网络”）的计算机模拟得以实现。细胞类型已被解释为基因组网络中的吸引子，其中网络架构倾向于在响应扰动时在稳定性和适应性之间实现平衡。基于使用离散动力学实验室（DDLab）软件的计算机模拟，描述了这些想法以及典型轨迹上的有序-混沌度量，这些度量进一步表征了网络动态。
