Science Advances

Supplementary Materials

This PDF file includes:

  • text S1. Network data sets
  • text S2. Motif clustering example
  • text S3. Analysis of random network models
  • fig. S1. FFL motifs extracted from the A. fulgidus metabolic network.
  • fig. S2. FFL motifs extracted from the E. coli metabolic network.
  • fig. S3. Expanded region of the A. fulgidus metabolic FFL motif cluster.
  • fig. S4. FFL and FBL motif clustering types across many networks of metabolism.
  • fig. S5. FFL motifs extracted from the transcriptional regulatory networks.
  • fig. S6. FFL motifs extracted from the Little Rock Lake food web.
  • fig. S7. FFL motifs extracted from the C. elegans neural network.
  • fig. S8. FFL motifs extracted from the Wikipedia vote network.
  • fig. S9. FFL motifs extracted from the air traffic control network.
  • fig. S10. FFL motifs extracted from the Gnutella file-sharing network.
  • fig. S11. FFL motifs extracted from the EU email network.
  • fig. S12. Robustness of FFL clustering distributions for a selection of real-world networks to varying amounts of random edge removal.
  • fig. S13. FFL motif clustering distributions for the Erdős-Rényi model.
  • fig. S14. FFL motif clustering type distributions for the Erdős-Rényi model.
  • fig. S15. Motif clustering type distributions for the node duplication model.
  • table S1. General network statistics for the real-world systems.
  • table S2. Motif-related statistics for FFLs in the real-world networks.
  • table S3. Statistics comparing the original and extracted FFLs for the real-world systems.
  • table S4. Results for motif clustering in random network models.
  • table S5. Structural analysis of duplicated E. coli operon candidates.
  • table S6. Essential EC numbers for the E. coli metabolic network.

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