{
  "annotation_id": "ec9b808f-9bcb-4e63-9e05-56d04d32ca98",
  "date_created": "2026-03-02T18:01:32.288000Z",
  "date_modified": "2026-03-02T18:01:32.288000Z",
  "file_hash": "eac972f05b0aff0ed7d0fa33f4e06f402114d9da52b2f9340f99b2d9d8109295",
  "private": false,
  "record": {
    "abstract": "A useful approach to the mathematical analysis of large-scale biological\nnetworks is based upon their decompositions into monotone dynamical systems.\nThis paper deals with two computational problems associated to finding\ndecompositions which are optimal in an appropriate sense. In graph-theoretic\nlanguage, the problems can be recast in terms of maximal sign-consistent\nsubgraphs. The theoretical results include polynomial-time approximation\nalgorithms as well as constant-ratio inapproximability results. One of the\nalgorithms, which has a worst-case guarantee of 87.9 percent from optimality,\nis based on the semidefinite programming relaxation approach of\nGoemans-Williamson. The algorithm was implemented and tested on a Drosophila\nsegmentation network and an Epidermal Growth Factor Receptor pathway model, and\nit was found to perform close to optimally.",
    "arxiv_id": "q-bio/0509040",
    "authors": [
      "Bhaskar DasGupta",
      "German Andres Enciso",
      "Eduardo Sontag",
      "Yi Zhang"
    ],
    "categories": [
      "q-bio.MN"
    ],
    "title": "Algorithmic and Complexity Results for Decompositions of Biological   Networks into Monotone Subsystems",
    "url": "https://arxiv.org/abs/q-bio/0509040"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c2e3560a-3aac-4210-81d7-20c3ee072798",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}