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Tsur2019 - Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm Lab

About lab

This is a simple mathematical model describing the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. It can be used to explore immune response dynamics and compare pathway-level behavior across conditions.

Runtime

Duration10
Comms Step1

Runs

Total0
Completed0
Failed0

Metadata

Packagetsur2019-predicting-response-to-pembrolizumab-in-metastatic-mela
Created2026-05-15
Updated2026-05-15
immunologysbmlbiomodels_ebifaithfulcuratedvisualisation

Manifest

{
  "io": {
    "inputs": [
      {
        "name": "initial_unresolved_prediction_observable_1",
        "label": "Initial Unresolved Prediction Observable 1",
        "units": "native source value",
        "default": 1,
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.initial_unresolved_prediction_observable_1",
        "description": "Initial Unresolved Prediction Observable 1. Sets the initial value of bundled SBML species `A_pc`."
      },
      {
        "name": "initial_unresolved_prediction_observable_2",
        "label": "Initial Unresolved Prediction Observable 2",
        "units": "native source value",
        "default": 1,
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.initial_unresolved_prediction_observable_2",
        "description": "Initial Unresolved Prediction Observable 2. Sets the initial value of bundled SBML species `T_il`."
      },
      {
        "name": "initial_unresolved_prediction_observable_3",
        "label": "Initial Unresolved Prediction Observable 3",
        "units": "native source value",
        "default": 50,
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.initial_unresolved_prediction_observable_3",
        "description": "Initial Unresolved Prediction Observable 3. Sets the initial value of bundled SBML species `M_el`."
      }
    ],
    "outputs": [
      {
        "name": "unresolved_prediction_observable_1",
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.unresolved_prediction_observable_1"
      },
      {
        "name": "unresolved_prediction_observable_2",
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.unresolved_prediction_observable_2"
      },
      {
        "name": "unresolved_prediction_observable_3",
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.unresolved_prediction_observable_3"
      },
      {
        "name": "state",
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.state"
      },
      {
        "name": "summary",
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.summary"
      },
      {
        "name": "species_labels",
        "maps_to": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.species_labels"
      }
    ]
  },
  "title": "Tsur2019 - Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm Lab",
  "models": [
    {
      "path": "owned/models/immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model",
      "alias": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model",
      "provenance": {
        "owned_path": "owned/models/immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model"
      }
    },
    {
      "path": "owned/models/visualisation",
      "alias": "visualisation",
      "provenance": {
        "owned_path": "owned/models/visualisation"
      }
    }
  ],
  "wiring": [
    {
      "to": [
        "visualisation.immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model_state"
      ],
      "from": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.state"
    },
    {
      "to": [
        "visualisation.immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model_summary"
      ],
      "from": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.summary"
    },
    {
      "to": [
        "visualisation.immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model_species_labels"
      ],
      "from": "immunology_sbml_tsur2019_predicting_response_to_pembrolizumab_in_model1911150001_model.species_labels"
    }
  ],
  "runtime": {
    "duration": 10,
    "initial_inputs": {},
    "communication_step": 1
  },
  "description": "This is a simple mathematical model describing the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. It can be used to explore immune response dynamics and compare pathway-level behavior across conditions.",
  "schema_version": "2.0"
}

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