Dynamics and processing in finite self-similar networks

被引:18
作者
DeDeo, Simon [1 ]
Krakauer, David C. [1 ,2 ,3 ]
机构
[1] Santa Fe Inst, Santa Fe, NM 87501 USA
[2] Univ Wisconsin, Dept Genet, Madison, WI 53706 USA
[3] Univ Wisconsin, Wisconsin Inst Discovery, Madison, WI 53706 USA
基金
美国国家科学基金会;
关键词
information-processing; phase transition; renormalization; computation; ISING-MODEL; HIERARCHICAL ORGANIZATION; COMMUNITY STRUCTURE; STATISTICAL-THEORY; CLUSTER-EXPANSION; RENORMALIZATION; CONNECTIVITY; INFORMATION; ROBUSTNESS; COMPLEXITY;
D O I
10.1098/rsif.2011.0840
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A common feature of biological networks is the geometrical property of self-similarity. Molecular regulatory networks through to circulatory systems, nervous systems, social systems and ecological trophic networks show self-similar connectivity at multiple scales. We analyse the relationship between topology and signalling in contrasting classes of such topologies. We find that networks differ in their ability to contain or propagate signals between arbitrary nodes in a network depending on whether they possess branching or loop-like features. Networks also differ in how they respond to noise, such that one allows for greater integration at high noise, and this performance is reversed at low noise. Surprisingly, small-world topologies, with diameters logarithmic in system size, have slower dynamical time scales, and may be less integrated (more modular) than networks with longer path lengths. All of these phenomena are essentially mesoscopic, vanishing in the infinite limit but producing strong effects at sizes and time scales relevant to biology.
引用
收藏
页码:2131 / 2144
页数:14
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