{
  "annotation_id": "115a09d5-c343-4a90-a2d9-e23ba748b00a",
  "date_created": "2026-03-02T18:01:35.408000Z",
  "date_modified": "2026-03-02T18:01:35.408000Z",
  "file_hash": "1a5d9c293b6f350fe7a809baae335b875ede19d88fe6ad51aa7f8301427356f3",
  "private": false,
  "record": {
    "abstract": "Boolean networks with canalizing functions are used to model gene regulatory\nnetworks. In order to learn how such networks may behave under evolutionary\nforces, we simulate the evolution of a single Boolean network by means of an\nadaptive walk, which allows us to explore the fitness landscape. Mutations\nchange the connections and the functions of the nodes. Our fitness criterion is\nthe robustness of the dynamical attractors against small perturbations. We find\nthat with this fitness criterion the global maximum is always reached and that\nthere is a huge neutral space of 100% fitness. Furthermore, in spite of having\nsuch a high degree of robustness, the evolved networks still share many\nfeatures with \"chaotic\" networks.",
    "arxiv_id": "q-bio/0701025",
    "authors": [
      "Agnes Szejka",
      "Barbara Drossel"
    ],
    "categories": [
      "q-bio.PE",
      "q-bio.MN"
    ],
    "doi": "10.1140/epjb/e2007-00135-2",
    "journal_ref": "Eur. Phys. J. B 56, 373-380 (2007)",
    "title": "Evolution of Canalizing Boolean Networks",
    "url": "https://arxiv.org/abs/q-bio/0701025"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "cacf812d-1cee-4c65-bb29-71fb14441e61",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}