{
  "annotation_id": "dfafb87e-cca9-40d0-9ab4-1ecaf03c6973",
  "date_created": "2026-03-02T18:01:06.545000Z",
  "date_modified": "2026-03-02T18:01:06.545000Z",
  "file_hash": "ecf3af5a3595bd8f079b4b8538af103065a48b09e03ab2d66a985f10dec5caf6",
  "private": false,
  "record": {
    "abstract": "We present a novel multiscale simulation approach for modeling stochasticity\nin chemical reaction networks. The approach seamlessly integrates\nexact-stochastic and \"leaping\" methodologies into a single \"partitioned\nleaping\" algorithmic framework. The technique correctly accounts for stochastic\nnoise at significantly reduced computational cost, requires the definition of\nonly three model-independent parameters and is particularly well-suited for\nsimulating systems containing widely disparate species populations. We present\nthe theoretical foundations of partitioned leaping, discuss various options for\nits practical implementation and demonstrate the utility of the method via\nillustrative examples.",
    "arxiv_id": "physics/0601217",
    "authors": [
      "Leonard A. Harris",
      "Paulette Clancy"
    ],
    "categories": [
      "physics.chem-ph",
      "physics.comp-ph",
      "q-bio.QM"
    ],
    "doi": "10.1063/1.2354085",
    "journal_ref": "J. Chem. Phys. 125 (2006) 144107",
    "title": "A \"partitioned leaping\" approach for multiscale modeling of chemical   reaction dynamics",
    "url": "https://arxiv.org/abs/physics/0601217"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9b067439-31e9-4917-8daf-56549785b4f7",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}