About lab
Yakimchuk2019 - Mathematical modeling of immune modulation by glucocorticoids Lab
Curated immunology lab using the bundled source model as the scientific source of truth.
What You'll See
This captured run documents the default Yakimchuk2019 - Mathematical modeling of immune modulation by glucocorticoids configuration for 10.0 time units with a 1.0 communication step. Default inputs include Initial Unresolved Immune Observable 1, Initial Unresolved Immune Observable 2, Initial Unresolved Immune Observable 3, and Initial Unresolved Immune Observable 4. Reported outputs include unresolved_immune_observable_1, unresolved_immune_observable_2, unresolved_immune_observable_3, and unresolved_immune_observable_4. The screenshots below pair the run-interpretation table with Immune-cell dynamics and Largest state excursions so the README shows both trajectories and the strongest state changes from the same dark-mode run.
Output Visualizations
The run-interpretation table summarizes the configured Yakimchuk2019 - Mathematical modeling of immune modulation by glucocorticoids simulation and its final-state diagnostics.
The Immune-cell dynamics time series follows the selected immune, pathogen, tumor, or signaling quantities across the simulated horizon.
The largest state excursions chart ranks the state variables that moved furthest during the run.
Mathematical modeling of immune modulation by glucocorticoidsKonstantin Yakimchukhttps://doi.org/10.1016/j.biosystems.2019.104066AbstractThe cellular and molecular mechanisms of immunomodulatory actio. It can be used to explore immune response dynamics and compare pathway-level behavior across conditions.
Runtime
Runs
Metadata
Manifest
{
"io": {
"inputs": [
{
"name": "initial_unresolved_immune_observable_1",
"label": "Initial Unresolved Immune Observable 1",
"units": "native source value",
"default": 1,
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.initial_unresolved_immune_observable_1",
"description": "Initial Unresolved Immune Observable 1. Sets the initial value of bundled SBML species `Te`."
},
{
"name": "initial_unresolved_immune_observable_2",
"label": "Initial Unresolved Immune Observable 2",
"units": "native source value",
"default": 1,
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.initial_unresolved_immune_observable_2",
"description": "Initial Unresolved Immune Observable 2. Sets the initial value of bundled SBML species `N`."
},
{
"name": "initial_unresolved_immune_observable_3",
"label": "Initial Unresolved Immune Observable 3",
"units": "native source value",
"default": 1,
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.initial_unresolved_immune_observable_3",
"description": "Initial Unresolved Immune Observable 3. Sets the initial value of bundled SBML species `A`."
},
{
"name": "initial_unresolved_immune_observable_4",
"label": "Initial Unresolved Immune Observable 4",
"units": "native source value",
"default": 1,
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.initial_unresolved_immune_observable_4",
"description": "Initial Unresolved Immune Observable 4. Sets the initial value of bundled SBML species `Tr`."
},
{
"name": "initial_unresolved_immune_observable_5",
"label": "Initial Unresolved Immune Observable 5",
"units": "native source value",
"default": 1,
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.initial_unresolved_immune_observable_5",
"description": "Initial Unresolved Immune Observable 5. Sets the initial value of bundled SBML species `T_dc`."
}
],
"outputs": [
{
"name": "unresolved_immune_observable_1",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.unresolved_immune_observable_1"
},
{
"name": "unresolved_immune_observable_2",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.unresolved_immune_observable_2"
},
{
"name": "unresolved_immune_observable_3",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.unresolved_immune_observable_3"
},
{
"name": "unresolved_immune_observable_4",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.unresolved_immune_observable_4"
},
{
"name": "unresolved_immune_observable_5",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.unresolved_immune_observable_5"
},
{
"name": "state",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.state"
},
{
"name": "summary",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.summary"
},
{
"name": "species_labels",
"maps_to": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.species_labels"
}
]
},
"title": "Yakimchuk2019 - Mathematical modeling of immune modulation by glucocorticoids Lab",
"models": [
{
"path": "owned/models/immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model",
"alias": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model",
"provenance": {
"owned_path": "owned/models/immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model"
}
},
{
"path": "owned/models/visualisation",
"alias": "visualisation",
"provenance": {
"owned_path": "owned/models/visualisation"
}
}
],
"wiring": [
{
"to": [
"visualisation.immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model_state"
],
"from": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.state"
},
{
"to": [
"visualisation.immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model_summary"
],
"from": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.summary"
},
{
"to": [
"visualisation.immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model_species_labels"
],
"from": "immunology_sbml_yakimchuk2019_mathematical_modeling_of_immune_mo_model1912170005_model.species_labels"
}
],
"runtime": {
"duration": 10,
"initial_inputs": {},
"communication_step": 1
},
"description": "Mathematical modeling of immune modulation by glucocorticoidsKonstantin Yakimchukhttps://doi.org/10.1016/j.biosystems.2019.104066AbstractThe cellular and molecular mechanisms of immunomodulatory actio. It can be used to explore immune response dynamics and compare pathway-level behavior across conditions.",
"schema_version": "2.0"
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