Yazdjer2019 - reinforcement learning-based control of tumor growth under anti-angiogenic therapy Lab

{
  "io": {
    "inputs": [
      {
        "name": "initial_tumor_volume_x_1",
        "label": "Tumor volume x 1",
        "units": "native SBML value",
        "default": 1,
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.initial_tumor_volume_x_1",
        "description": "Initial Tumor volume x 1. Sets the initial value of bundled SBML symbol `tumor_volume_x_1`."
      },
      {
        "name": "initial_endothelial_volume_x_2",
        "label": "Endothelial volume x 2",
        "units": "native SBML value",
        "default": 1,
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.initial_endothelial_volume_x_2",
        "description": "Initial Endothelial volume x 2. Sets the initial value of bundled SBML symbol `endothelial_volume_x_2`."
      },
      {
        "name": "initial_concentration_of_administrated_inhibitor_x_3",
        "label": "Concentration of administrated inhibitor x 3",
        "units": "native SBML value",
        "default": 0,
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.initial_concentration_of_administrated_inhibitor_x_3",
        "description": "Initial Concentration of administrated inhibitor x 3. Sets the initial value of bundled SBML symbol `concentration_of_administrated_inhibitor_x_3`."
      }
    ],
    "outputs": [
      {
        "name": "tumor_volume_x_1",
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.tumor_volume_x_1",
        "description": "Tumor volume x 1 observable. Maps to SBML symbol `tumor_volume_x_1`."
      },
      {
        "name": "endothelial_volume_x_2",
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.endothelial_volume_x_2",
        "description": "Endothelial volume x 2 observable. Maps to SBML symbol `endothelial_volume_x_2`."
      },
      {
        "name": "concentration_of_administrated_inhibitor_x_3",
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.concentration_of_administrated_inhibitor_x_3",
        "description": "Concentration of administrated inhibitor x 3 observable. Maps to SBML symbol `concentration_of_administrated_inhibitor_x_3`."
      },
      {
        "name": "state",
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.state",
        "description": "Full raw SBML observable record for reproducibility and downstream visualisation."
      },
      {
        "name": "summary",
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.summary",
        "description": "Change and peak summary across the simulated SBML observables."
      },
      {
        "name": "species_labels",
        "maps_to": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.species_labels",
        "description": "Mapping from selected raw SBML observable symbols to display labels."
      }
    ]
  },
  "tags": [
    "biomodels_ebi",
    "curated",
    "drug-response",
    "faithful",
    "oncology",
    "pharmacology",
    "physiology",
    "sbml",
    "systemsbiology",
    "tumor-growth"
  ],
  "title": "Yazdjer2019 - reinforcement learning-based control of tumor growth under anti-angiogenic therapy Lab",
  "models": [
    {
      "path": "models/core",
      "alias": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model"
    },
    {
      "path": "models/visualisation",
      "alias": "visualisation"
    }
  ],
  "wiring": [
    {
      "to": [
        "visualisation.oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model_state"
      ],
      "from": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.state"
    },
    {
      "to": [
        "visualisation.oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model_summary"
      ],
      "from": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.summary"
    },
    {
      "to": [
        "visualisation.oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model_species_labels"
      ],
      "from": "oncology_sbml_yazdjer2019_reinforcement_learning_based_control_biomd0000000821_model.species_labels"
    }
  ],
  "package": "yazdjer2019-reinforcement-learning-based-control-of-tum-5f0a640b",
  "runtime": {
    "duration": 10,
    "initial_inputs": {},
    "communication_step": 1
  },
  "version": "1.0.0",
  "description": "This model is based on:Reinforcement learning-based control of tumor growth under anti-angiogenic therapyAuthors: Parisa Yazdjerdi, Nader Meskin, Mohammad Al-Naemi, Ala-Eddin Al Moustafa, Levente Kova. It can be used to explore tumor-related dynamics and compare treatment-response behavior across conditions.",
  "schema_version": "2.0"
}