{
  "annotation_id": "6fd19c8c-ba9c-4295-ac48-02b2f5527b60",
  "date_created": "2026-03-02T18:01:31.997000Z",
  "date_modified": "2026-03-02T18:01:31.997000Z",
  "file_hash": "2e18186b4d442b05eb722925d7ef09e54a2c9256a5074f9e211b6e6eef4c9050",
  "private": false,
  "record": {
    "abstract": "We study a class of growth algorithms for directed graphs that are candidate\nmodels for the evolution of genetic regulatory networks. The algorithms involve\npartial duplication of nodes and their links, together with innovation of new\nlinks, allowing for the possibility that input and output links from a newly\ncreated node may have different probabilities of survival. We find some\ncounterintuitive trends as parameters are varied, including the broadening of\nindegree distribution when the probability for retaining input links is\ndecreased. We also find that both the scaling of transcription factors with\ngenome size and the measured degree distributions for genes in yeast can be\nreproduced by the growth algorithm if and only if a special seed is used to\ninitiate the process.",
    "arxiv_id": "q-bio/0510009",
    "authors": [
      "D. V. Foster",
      "S. A. Kauffman",
      "J. E. S. Socolar"
    ],
    "categories": [
      "q-bio.MN"
    ],
    "doi": "10.1103/PhysRevE.73.031912",
    "journal_ref": "Phys. Rev. E, Vol. 73, 031912 (2006)",
    "title": "Network growth models and genetic regulatory networks",
    "url": "https://arxiv.org/abs/q-bio/0510009"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "68b6b67a-7ef2-4e8c-aaff-a54cdf798077",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}