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J R Soc Interface. 2014 Apr 30;11(96):20140283. doi: 10.1098/rsif.2014.0283. Print 2014 Jul 6.

Topological properties of robust biological and computational networks.

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Machine Learning Department, Carnegie Mellon University, , Pittsburgh, PA 15213, USA.


Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields.


biological networks; communication networks; robustness; security

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