Shin_2018_EGFR-PYK2-c-Met interaction network_model Lab

{
  "io": {
    "inputs": [
      {
        "name": "egfrtot_source_parameter",
        "label": "EGFRtot source parameter",
        "units": "native SBML value",
        "default": 398.107,
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.egfrtot_source_parameter",
        "description": "EGFRtot source parameter. Maps to bundled SBML parameter `EGFRtot`."
      },
      {
        "name": "ca_egf_source_parameter",
        "label": "CaEGF source parameter",
        "units": "native SBML value",
        "default": 0.0891251,
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.ca_egf_source_parameter",
        "description": "CaEGF source parameter. Maps to bundled SBML parameter `caEGF`."
      },
      {
        "name": "egf_source_parameter",
        "label": "EGF source parameter",
        "units": "native SBML value",
        "default": 10,
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.egf_source_parameter",
        "description": "EGF source parameter. Maps to bundled SBML parameter `EGF`."
      },
      {
        "name": "initial_pegfr",
        "label": "PEGFR",
        "units": "native SBML value",
        "default": 0.109014,
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.initial_pegfr",
        "description": "Initial PEGFR. Sets the initial value of bundled SBML symbol `pEGFR`."
      },
      {
        "name": "initial_egfrub",
        "label": "EGFRub",
        "units": "native SBML value",
        "default": 6.93991,
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.initial_egfrub",
        "description": "Initial EGFRub. Sets the initial value of bundled SBML symbol `EGFRub`."
      },
      {
        "name": "initial_perk",
        "label": "PERK",
        "units": "native SBML value",
        "default": 0.669043,
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.initial_perk",
        "description": "Initial PERK. Sets the initial value of bundled SBML symbol `pERK`."
      }
    ],
    "outputs": [
      {
        "name": "pegfr",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.pegfr",
        "description": "PEGFR observable. Maps to SBML symbol `pEGFR`."
      },
      {
        "name": "egfrub",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.egfrub",
        "description": "EGFRub observable. Maps to SBML symbol `EGFRub`."
      },
      {
        "name": "perk",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.perk",
        "description": "PERK observable. Maps to SBML symbol `pERK`."
      },
      {
        "name": "pstat3",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.pstat3",
        "description": "PSTAT3 observable. Maps to SBML symbol `pSTAT3`."
      },
      {
        "name": "stat3u_stattic",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.stat3u_stattic",
        "description": "STAT3uStattic observable. Maps to SBML symbol `STAT3uStattic`."
      },
      {
        "name": "pyk2",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.pyk2",
        "description": "PYK2 observable. Maps to SBML symbol `PYK2`."
      },
      {
        "name": "state",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.state",
        "description": "Full raw SBML observable record for reproducibility and downstream visualisation."
      },
      {
        "name": "summary",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.summary",
        "description": "Change and peak summary across the simulated SBML observables."
      },
      {
        "name": "species_labels",
        "maps_to": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_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",
    "signal-transduction",
    "signaling",
    "systemsbiology",
    "tumor-growth"
  ],
  "title": "Shin_2018_EGFR-PYK2-c-Met interaction network_model Lab",
  "models": [
    {
      "path": "models/core",
      "alias": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model"
    },
    {
      "path": "models/visualisation",
      "alias": "visualisation"
    }
  ],
  "wiring": [
    {
      "to": [
        "visualisation.oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model_state"
      ],
      "from": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.state"
    },
    {
      "to": [
        "visualisation.oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model_summary"
      ],
      "from": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.summary"
    },
    {
      "to": [
        "visualisation.oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model_species_labels"
      ],
      "from": "oncology_sbml_shin_2018_egfr_pyk2_c_met_interaction_network_mo_biomd0000000826_model.species_labels"
    }
  ],
  "package": "shin-2018-egfr-pyk2-c-met-interaction-network-model",
  "runtime": {
    "duration": 10,
    "initial_inputs": {},
    "communication_step": 1
  },
  "version": "1.0.0",
  "description": "Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for Triple-negative breast cancer. It can be used to explore tumor-related dynamics and compare treatment-response behavior across conditions.",
  "schema_version": "2.0"
}