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Sci. Signal., 31 May 2011
[DOI: 10.1126/scisignal.2001390]

Supplementary Materials for:

Reduction of Complex Signaling Networks to a Representative Kernel

Jeong-Rae Kim, Junil Kim, Yung-Keun Kwon, Hwang-Yeol Lee, Pat Heslop-Harrison, Kwang-Hyun Cho*

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

This PDF file includes:

  • Model Descriptions
  • Fig. S1. The multidimensional scaling map for classification of responses of the nonlinear (Hill-type) models of two- and three-node networks.
  • Fig. S2. The flow diagram illustrating the kernel identification algorithm.
  • Fig. S3. Ratios of kernel to original in terms of nodes and edges for the signaling networks of E. coli, yeast, and human.
  • Fig. S4. Relative size of the giant component, which is the component with the most connections, in the original three networks.
  • Fig. S5. The frequency distributions of three-node subnetworks in the signaling networks of E. coli and yeast compared with the distributions of these subnetworks in their kernels.
  • Fig. S6. Average indegrees and outdegrees of kinases in the original networks and kernels for the networks of E. coli, yeast, and human.
  • Fig. S7. The frequency of essential genes and disease genes in the kernel nodes and non–kernel nodes.
  • Fig. S8. The ratio of essential genes contained in the kernel and non–kernel nodes of the networks of E. coli and yeast.
  • Fig. S9. The ratio of essential genes represented in the set of nodes of each degree in the human network.
  • Fig. S10. Degree distribution and cumulative frequency distribution of degrees in the human network.
  • Fig. S11. The degree in the human protein-protein interaction network versus the degree in the human signaling network.
  • Fig. S12. Comparison of the input-output dynamics of the original network and the reduced network after applying five different network-reduction approaches.
  • Fig. S13. The stimulus pattern used for the simulation of two- and three-node network models.
  • Fig. S14. Six representative response patterns used for classification of two- and three-node networks.
  • Table S1. Response coherency between the original signaling network and the corresponding kernel.
  • Table S2. GO terms related to genes represented by nodes in the kernels (kernel genes).
  • Table S3. GO terms related to genes that were excluded from the kernel, but were represented by nodes in the original network (non-kernel).
  • Table S4. GO terms related to the kernel genes that had evolutionary rates larger than 0.25.
  • Table S5. GO terms related to the kernel genes that had evolutionary rates between 0.125 and 0.25.
  • Table S6. GO terms related to the kernel genes that had evolutionary rates between 0.0625 and 0.125.
  • Table S7. GO terms related to the kernel genes that had evolutionary rates less than 0.0625.
  • Table S8. GO terms related to non–kernel genes that had evolutionary rates less than 0.25.
  • Table S9. GO terms related to the non–kernel genes that had evolutionary rates between 0.125 and 0.25.
  • Table S10. Simulation results for linear models of 18 network structures.
  • Table S11. Simulation results for Hill-type models of 18 network structures.
  • Table S12. The list of 20 species examined in the HomoloGene database.
  • References

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Other Supplementary Material for this manuscript includes the following:

  • Data S1. The circadian regulatory network data where the first, second, and third columns denote regulator, relation, and target, respectively. [Filename: Circadian_regulatory_network.txt]
  • Data S2. The integrin signaling pathway data where the first, second, and third columns denote regulator, relation, and target, respectively. [Filename: Integrin_signaling_pathway.txt]
  • Data S3. The E. coli signaling network data where the first, second, and third columns denote regulator, relation, and target, respectively. [Filename: Ecoli_network.txt]
  • Data S4. The yeast signaling network data where the first, second, and third columns denote regulator, relation, and target, respectively. [Filename: Yeast_network.txt]
  • Data S5. The human signaling network data where the first, second, and third columns denote regulator, relation, and target, respectively. [Filename: Human_network.txt]
  • Software. The software of the proposed kernel identification algorithm for MS-DOS–type operating systems. [Filename: kernelfinder.exe]

[Download Data Files S1-S5 and Software (Compressed)]

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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).

© 2011 American Association for the Advancement of Science


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