{
  "annotation_id": "0dcf8c0f-af24-49c8-9509-482862f6a6b2",
  "date_created": "2026-03-02T18:01:35.016000Z",
  "date_modified": "2026-03-02T18:01:35.016000Z",
  "file_hash": "b3e86cc7a5122b82b80f36a16cdd253dcba29a61ecc41ff9e000b793b0214baa",
  "private": false,
  "record": {
    "abstract": "We present a Perfect Sampling algorithm that can be applied to the Master\nEquation of Gene Regulatory Networks (GRNs). The method recasts Gillespie\u0027s\nStochastic Simulation Algorithm (SSA) in the light of Markov Chain Monte Carlo\nmethods and combines it with the Dominated Coupling From The Past (DCFTP)\nalgorithm to provide guaranteed sampling from the stationary distribution. We\nshow how the DCFTP-SSA can be generically applied to genetic networks with\nfeedback formed by the interconnection of linear enzymatic reactions and\nnonlinear Monod- and Hill-type elements. We establish rigorous bounds on the\nerror and convergence of the DCFTP-SSA, as compared to the standard SSA,\nthrough a set of increasingly complex examples. Once the building blocks for\nGRNs have been introduced, the algorithm is applied to study properly averaged\ndynamic properties of two experimentally relevant genetic networks: the toggle\nswitch, a two-dimensional bistable system, and the repressilator, a\nsix-dimensional genetic oscillator.",
    "arxiv_id": "q-bio/0610050",
    "authors": [
      "Martin Hemberg",
      "Mauricio Barahona"
    ],
    "categories": [
      "q-bio.QM",
      "q-bio.GN"
    ],
    "doi": "10.1529/biophysj.106.099390",
    "title": "Perfect Sampling of the Master Equation for Gene Regulatory Networks",
    "url": "https://arxiv.org/abs/q-bio/0610050"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "caa525f4-52a7-4623-bb1b-ebe338149ee4",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}