{
  "annotation_id": "efdb2012-c0d7-4e30-a657-f6ae658a1c19",
  "date_created": "2026-03-02T18:01:35.334000Z",
  "date_modified": "2026-03-02T18:01:35.334000Z",
  "file_hash": "0363394285b107772136cee94b5ecabe6a90efb229d846924c8e22396592e4bf",
  "private": false,
  "record": {
    "abstract": "In this paper, we propose a mean-field model which attempts to bridge the gap\nbetween random Boolean networks and more realistic stochastic modeling of\ngenetic regulatory networks. The main idea of the model is to replace all\nregulatory interactions to any one gene with an average or effective\ninteraction, which takes into account the repression and activation mechanisms.\nWe find that depending on the set of regulatory parameters, the model exhibits\nrich nonlinear dynamics. The model also provides quantitative support to the\nearlier qualitative results obtained for random Boolean networks.",
    "arxiv_id": "q-bio/0606022",
    "authors": [
      "M. Andrecut",
      "S. A. Kauffman"
    ],
    "categories": [
      "q-bio.QM",
      "q-bio.GN"
    ],
    "doi": "10.1088/1367-2630/8/8/148",
    "title": "Mean Field Model of Genetic Regulatory Networks",
    "url": "https://arxiv.org/abs/q-bio/0606022"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "927222ae-4449-4bb7-8d9d-c8e42c86888f",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}