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


Reduction of Complex Signaling Networks to a Representative Kernel

Jeong-Rae Kim1,2, Junil Kim1, Yung-Keun Kwon1,3, Hwang-Yeol Lee1, Pat Heslop-Harrison4, and Kwang-Hyun Cho1*

1 Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.
2 Department of Mathematics, University of Seoul, Seoul 130-743, Republic of Korea.
3 School of Computer Science and Information Technology, University of Ulsan, Ulsan 680-749, Republic of Korea.
4 Department of Biology, University of Leicester, Leicester LE1 7RH, UK.

Abstract: The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a "kernel" that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network’s kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.

* To whom correspondence should be addressed. E-mail: ckh{at}

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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