About lab
Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law
This Biosimulant lab wraps Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law as a runnable oncology model with a companion visualization module.
This is a mathematical model using a Gompertz growth law to describe the in vivo dynamics of a cancer under treatment with an oncolytic virus. It can be used to explore treatment-response dynamics and compare scenario outcomes across configurations.
What You'll See
The lab asks: How does the bundled treatment-response model evolve under its baseline source conditions? It runs for 10.0 time units with a communication step of 1.0. The run uses the model defaults declared by the curated SBML wrapper. The generated visualizations focus on U, I, and V, combining trajectory, endpoint-comparison, and summary-table views from one completed dark-mode run.
In this captured run, I peaked at 124.8 and I moved by 24.800 native units across 10.0 simulation windows.
Output Visualizations
Summary table for Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law, reporting the scientific question, observed answer (largest change: I at 24.800 native units), evidence (peak observable: I), dominant module, and caveat.
Trajectories of U, I, and V across the 10.0 simulation. In this run I climbed from 100.0 to 124.8 and U fell from 75.000 to 63.424 — the largest movements among the focused observables.
Endpoint ranking of the focused observables. Top 3 by final value: I = 124.8, U = 63.424, V = 10.881.
Model Context
- Core model:
models/core - Visualization model:
models/visualisation - Standard:
other - Upstream source:
biomodels_ebi:BIOMD0000000850 - License:
CC0 - Visual scope: Drug/treatment response and tumour-state change
- Caveat: Values are native SBML quantities; the cleanup does not reinterpret source equations.
Inputs
| Input | Maps To | Default | Notes |
|---|
Outputs
| Output | Maps To | Role |
|---|---|---|
model_state_1 | oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.model_state_1 | U observable. |
model_state_2 | oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.model_state_2 | I observable. |
model_state_3 | oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.model_state_3 | V observable. |
state | oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.state | Full raw SBML observable record for reproducibility and downstream visualisation. |
summary | oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.summary | Change and peak summary across the simulated SBML observables. |
species_labels | oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.species_labels | Mapping from selected raw SBML observable symbols to display labels. |
Runtime
- Duration:
10.0 - Communication step:
1.0
Running Locally
biosimulant labs serve .
This is a mathematical model using a Gompertz growth law to describe the in vivo dynamics of a cancer under treatment with an oncolytic virus. It can be used to explore tumor-related dynamics and compare treatment-response behavior across conditions.
Runtime
Runs
Metadata
Manifest
{
"io": {
"inputs": [
{
"name": "initial_uninfected_tumor_cells",
"label": "Uninfected Tumor Cells",
"units": "native SBML value",
"default": 75,
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.initial_uninfected_tumor_cells",
"description": "Initial Uninfected Tumor Cells. Sets the initial value of bundled SBML symbol `U`."
},
{
"name": "initial_infected_tumor_cells",
"label": "Infected Tumor Cells",
"units": "native SBML value",
"default": 100,
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.initial_infected_tumor_cells",
"description": "Initial Infected Tumor Cells. Sets the initial value of bundled SBML symbol `I`."
},
{
"name": "initial_virus_load",
"label": "Virus Load",
"units": "native SBML value",
"default": 10,
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.initial_virus_load",
"description": "Initial Virus Load. Sets the initial value of bundled SBML symbol `V`."
}
],
"outputs": [
{
"name": "uninfected_tumor_cells",
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.uninfected_tumor_cells",
"description": "Uninfected Tumor Cells observable. Maps to SBML symbol `U`."
},
{
"name": "infected_tumor_cells",
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.infected_tumor_cells",
"description": "Infected Tumor Cells observable. Maps to SBML symbol `I`."
},
{
"name": "virus_load",
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.virus_load",
"description": "Virus Load observable. Maps to SBML symbol `V`."
},
{
"name": "state",
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.state",
"description": "Full raw SBML observable record for reproducibility and downstream visualisation."
},
{
"name": "summary",
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.summary",
"description": "Change and peak summary across the simulated SBML observables."
},
{
"name": "species_labels",
"maps_to": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.species_labels",
"description": "Mapping from selected raw SBML observable symbols to display labels."
}
]
},
"tags": [
"biomodels_ebi",
"curated",
"drug-response",
"faithful",
"immunotherapy",
"oncology",
"pharmacology",
"physiology",
"sbml",
"systemsbiology",
"tumor-growth",
"virotherapy"
],
"title": "Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law Lab",
"models": [
{
"path": "owned/models/oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model",
"alias": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model",
"provenance": {
"owned_path": "owned/models/oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model"
}
},
{
"path": "owned/models/visualisation",
"alias": "visualisation",
"provenance": {
"owned_path": "owned/models/visualisation"
}
}
],
"wiring": [
{
"to": [
"visualisation.oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model_state"
],
"from": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.state"
},
{
"to": [
"visualisation.oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model_summary"
],
"from": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.summary"
},
{
"to": [
"visualisation.oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model_species_labels"
],
"from": "oncology_sbml_jenner2019_oncolytic_virotherapy_for_tumours_fol_biomd0000000850_model.species_labels"
}
],
"runtime": {
"duration": 10,
"initial_inputs": {},
"communication_step": 1
},
"description": "This is a mathematical model using a Gompertz growth law to describe the in vivo dynamics of a cancer under treatment with an oncolytic virus. It can be used to explore tumor-related dynamics and compare treatment-response behavior across conditions.",
"schema_version": "2.0"
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