Dwight Kuo P, Banzhaf Wolfgang, Leier André
Department of Computer Science, Memorial University of Newfoundland, St John's, NL, Canada.
Biosystems. 2006 Sep;85(3):177-200. doi: 10.1016/j.biosystems.2006.01.004. Epub 2006 May 2.
Topological measures of large-scale complex networks are applied to a specific artificial regulatory network model created through a whole genome duplication and divergence mechanism. This class of networks share topological features with natural transcriptional regulatory networks. Specifically, these networks display scale-free and small-world topology and possess subgraph distributions similar to those of natural networks. Thus, the topologies inherent in natural networks may be in part due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks is also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model.
大规模复杂网络的拓扑度量被应用于通过全基因组复制和分化机制创建的特定人工调控网络模型。这类网络与自然转录调控网络具有共同的拓扑特征。具体而言，这些网络呈现无标度和小世界拓扑结构，并且拥有与自然网络相似的子图分布。因此，自然网络中固有的拓扑结构可能部分归因于其创建方式，而非完全由选择作用下的后续进化所塑造。通过在模拟中对网络进行演化以获得三种简单类型的输出动态，还研究了这些网络动态的可进化性。从这个过程中获得的网络展现出各种各样的拓扑结构和基因数量，这表明在该模型中演化这类动态相对容易。
