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Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou Lab

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Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou

This Biosimulant lab wraps Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou as a runnable systems biology model with a companion visualization module. Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers Note: The SBML model is generated from SimBiology. It can be used to explore the configured dynamics and compare scenario outcomes across configurations.

What You'll See

The lab asks: Which gene-regulatory states dominate the source model trajectory? Source model: Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers. It runs for 1.0 time units with a communication step of 0.1. The run uses the model defaults declared by the curated SBML wrapper. The generated visualizations focus on mIIa_ATIII, mIIa, Xa_Va_II, Xa_Va, Xa_TFPI, and Xa_ATIII, combining trajectory, endpoint-comparison, and summary-table views from one completed dark-mode run.

In this captured run, Xa_ATIII moved from 0 to 1.16e-09 across 1.0 simulation windows.

Output Visualizations

Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou - run interpretation

Summary table for Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou, reporting the scientific question, observed answer, dominant module, and caveat.

Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou - timeseries visualization

Trajectories of Xa_ATIII, Xa_TFPI, mIIa, Xa_Va, Xa_Va_II, and mIIa_ATIII across the 1.0 simulation. In this run Xa_ATIII climbed from 0 to 1.16e-09 — the largest movements among the focused observables.

Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou - excursions bar

Largest-excursion ranking of the focused observables — the absolute movement magnitude during the run. Top 3: Xa_ATIII = 1.16e-09, Xa_TFPI = 3.63e-10, mIIa = 4.4e-19, with 3 more observables below.

Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou - endpoint snapshot bar

Endpoint snapshot of the focused observables — final values from the captured run. Top 3 by value: Xa_ATIII = 1.16e-09, Xa_TFPI = 3.63e-10, mIIa = 4.4e-19, with 3 more observables below.

Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou - visualization

Visualization card from the Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou dark-mode run.

Model Context

  • Core model: models/core
  • Visualization model: models/visualisation
  • Standard: other
  • Upstream source: biomodels_ebi:BIOMD0000000611
  • License: CC0

Inputs

InputMaps ToDefaultNotes
Initial M I Ia Atiiisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_m_i_ia_atiiiSource state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol mIIa_ATIII.
Initial M I Iasystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_m_i_iaSource state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol mIIa.
Initial Xa Va Iisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_va_iiSource state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol Xa_Va_II.
Initial Xa Vasystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_vaSource state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol Xa_Va.
Initial Xa Tfpisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_tfpiSource state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol Xa_TFPI.
Initial Xa Atiiisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_atiiiSource state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol Xa_ATIII.

Outputs

OutputMaps ToRole
statesystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.stateAvailable to the visualization model and downstream workflows.
summarysystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.summaryAvailable to the visualization model and downstream workflows.
species_labelssystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.species_labelsAvailable to the visualization model and downstream workflows.
m_i_ia_atiiisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.m_i_ia_atiiiAvailable to the visualization model and downstream workflows.
m_i_iasystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.m_i_iaAvailable to the visualization model and downstream workflows.
xa_va_iisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_va_iiAvailable to the visualization model and downstream workflows.
xa_vasystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_vaAvailable to the visualization model and downstream workflows.
xa_tfpisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_tfpiAvailable to the visualization model and downstream workflows.
xa_atiiisystemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_atiiiAvailable to the visualization model and downstream workflows.

Runtime

  • Duration: 1.0
  • Communication step: 0.1

Running Locally

biosimulant labs serve

Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers Note: The SBML model is generated from SimBiology. The SimBiology (.sbproj) file is available for down.

Runtime

Duration1
Comms Step0.1

Runs

Total0
Completed0
Failed0

Metadata

Packagenayak2015-blood-coagulation-network-predicting-the-effects-of-va
Created2026-05-17
Updated2026-05-17
generegulationsystemsbiologysbmlbiomodels_ebifaithfulcuratedvisualisation

Manifest

{
  "io": {
    "inputs": [
      {
        "name": "initial_m_i_ia_atiii",
        "label": "Initial M I Ia Atiii",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_m_i_ia_atiii",
        "description": "Source state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol `mIIa_ATIII`."
      },
      {
        "name": "initial_m_i_ia",
        "label": "Initial M I Ia",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_m_i_ia",
        "description": "Source state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol `mIIa`."
      },
      {
        "name": "initial_xa_va_ii",
        "label": "Initial Xa Va Ii",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_va_ii",
        "description": "Source state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol `Xa_Va_II`."
      },
      {
        "name": "initial_xa_va",
        "label": "Initial Xa Va",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_va",
        "description": "Source state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol `Xa_Va`."
      },
      {
        "name": "initial_xa_tfpi",
        "label": "Initial Xa Tfpi",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_tfpi",
        "description": "Source state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol `Xa_TFPI`."
      },
      {
        "name": "initial_xa_atiii",
        "label": "Initial Xa Atiii",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.initial_xa_atiii",
        "description": "Source state initial condition exposed as a model-specific control because no explicit intervention parameter is identifiable. Maps to SBML symbol `Xa_ATIII`."
      }
    ],
    "outputs": [
      {
        "name": "state",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.state"
      },
      {
        "name": "summary",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.summary"
      },
      {
        "name": "species_labels",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.species_labels"
      },
      {
        "name": "m_i_ia_atiii",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.m_i_ia_atiii"
      },
      {
        "name": "m_i_ia",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.m_i_ia"
      },
      {
        "name": "xa_va_ii",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_va_ii"
      },
      {
        "name": "xa_va",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_va"
      },
      {
        "name": "xa_tfpi",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_tfpi"
      },
      {
        "name": "xa_atiii",
        "maps_to": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.xa_atiii"
      }
    ]
  },
  "tags": [
    "generegulation",
    "systemsbiology",
    "sbml",
    "biomodels_ebi",
    "faithful",
    "curated"
  ],
  "title": "Nayak2015 Blood Coagulation Network Predicting The Effects Of Variou Lab",
  "models": [
    {
      "path": "owned/models/systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model",
      "alias": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model",
      "provenance": {
        "owned_path": "owned/models/systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model"
      }
    },
    {
      "path": "owned/models/visualisation",
      "alias": "visualisation",
      "provenance": {
        "owned_path": "owned/models/visualisation"
      }
    }
  ],
  "wiring": [
    {
      "to": [
        "visualisation.systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model_state"
      ],
      "from": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.state"
    },
    {
      "to": [
        "visualisation.systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model_summary"
      ],
      "from": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.summary"
    },
    {
      "to": [
        "visualisation.systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model_species_labels"
      ],
      "from": "systemsbiology_sbml_nayak2015_blood_coagulation_network_predicting_t_biomd0000000611_model.species_labels"
    }
  ],
  "runtime": {
    "duration": 1,
    "initial_inputs": {},
    "communication_step": 0.1
  },
  "description": "Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers Note: The SBML model is generated from SimBiology. The SimBiology (.sbproj) file is available for down.",
  "schema_version": "2.0"
}

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