{
  "annotation_id": "aef088d3-e9cc-4e16-9160-1d6e7c67489f",
  "date_created": "2026-03-02T18:01:35.067000Z",
  "date_modified": "2026-03-02T18:01:35.067000Z",
  "file_hash": "02b681ebf7995196a1086defebaa8f89121f9a48d4be64da01482b3bdd53dbd2",
  "private": false,
  "record": {
    "abstract": "We propose the concepts of distributed robustness and r-robustness, well\nadapted to functional genetics. Then we discuss the robustness of the\nrelaxation time using a chemical reaction description of genetic and signalling\nnetworks. First, we obtain the following result for linear networks: for large\nmultiscale systems with hierarchical distribution of time scales the variance\nof the inverse relaxation time (as well as the variance of the stationary rate)\nis much lower than the variance of the separate constants. Moreover, it can\ntend to 0 faster than 1/n, where n is the number of reactions. We argue that\nsimilar phenomena are valid in the nonlinear case as well. As a numerical\nillustration we use a model of signalling network that can be applied to\nimportant transcription factors such as NFkB.",
    "arxiv_id": "q-bio/0701020",
    "authors": [
      "A. N. Gorban",
      "O. Radulescu"
    ],
    "categories": [
      "q-bio.MN"
    ],
    "doi": "10.1049/iet-syb:20060083",
    "journal_ref": "IET Syst. Biol., 2007, 1, (4), pp. 238--246",
    "title": "Dynamical robustness of biological networks with hierarchical   distribution of time scales",
    "url": "https://arxiv.org/abs/q-bio/0701020"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9138d501-771b-4068-adfc-4240e249353a",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}