{
  "annotation_id": "cf49680c-147b-4bd1-b18e-6183cdb1c31c",
  "date_created": "2026-03-02T18:01:31.348000Z",
  "date_modified": "2026-03-02T18:01:31.348000Z",
  "file_hash": "118dcc5049e8f8bf1ca9b8e5df2e9af8afbb7dbc63ef68eb6ebc27234dfc559f",
  "private": false,
  "record": {
    "abstract": "A quantitative measure of stability in stochastic dynamics starts to emerge\nin recent experiments on bioswitches. This quantity, similar to the potential\nfunction in mathematics, is deeply rooted in biology, dated back at the\nbeginning of quantitative description of biological processes: the adaptive\nlandscape of Wright (1932) and the development landscape of Waddington (1940).\nNevertheless, its quantitative implication has been frequently challenged by\nbiologists. Recent progresses in quantitative biology begin to meet those\noutstanding challenges.",
    "arxiv_id": "q-bio/0507009",
    "authors": [
      "P. Ao"
    ],
    "categories": [
      "q-bio.QM"
    ],
    "title": "Quantitative Measure of Stability in Gene Regulatory Networks",
    "url": "https://arxiv.org/abs/q-bio/0507009"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "2da14408-825b-4c1a-8bef-c3613686c59c",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}