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Sci. Signal., 31 May 2011
Vol. 4, Issue 175, p. ra35
[DOI: 10.1126/scisignal.2001390]


Editor's Summary

Reducing Complexity
The large and complex nature of the biochemical regulatory networks that govern cell behavior provides a major challenge to the systematic analysis of cell signaling. However, most processes that reduce network complexity fail to reproduce the dynamic properties of the original network. Kim et al. describe an algorithmic approach to network reduction and simplification that preserves the dynamics of the network. They applied their approach to several networks in species from bacteria to humans, producing simplified networks called "kernels." Examination of the genes represented by the kernel nodes provided insight into the evolution of these core network genes. Furthermore, the genes represented by the kernel nodes were enriched in disease-associated genes and drug targets, suggesting that this type of analysis may be therapeutically beneficial.

Citation: J.-R. Kim, J. Kim, Y.-K. Kwon, H.-Y. Lee, P. Heslop-Harrison, K.-H. Cho, Reduction of Complex Signaling Networks to a Representative Kernel. Sci. Signal. 4, ra35 (2011).

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