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Smallbone2013 - Metabolic Control Analysis - Example 2
Cleaned metabolism SBML ODE lab. The bundled SBML file remains the scientific source of truth.
Validation evidence: Tellurium loaded and simulated 3 floating species
What You'll See
These dark-mode screenshots show the default Smallbone2013 - Metabolic Control Analysis - Example 2 run over 10 model-time units with outputs sampled every 1. The lab exposes 3 inputs (initial_metabolic_pathway_state_1, initial_metabolic_pathway_state_2, initial_metabolic_pathway_state_3) and 6 outputs (metabolic_pathway_state_1, metabolic_pathway_state_2, metabolic_pathway_state_3, observable_values, run_summary, and 1 more). The default input state includes initial_metabolic_pathway_state_1=0.05625738310526, initial_metabolic_pathway_state_2=0.76876151899652, initial_metabolic_pathway_state_3=4.23123848100348. Smallbone2013 - Metabolic Control Analysis - Example 2 Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network paramet. It can be used to explore metabolic flux dynamics and compare pathway behavior across conditions.
Output Visualizations
The run interpretation table summarizes the configured Smallbone2013 - Metabolic Control Analysis - Example 2 simulation and its reported output statistics.
The observable dynamics plot traces the main reported outputs over the captured run window, including metabolic_pathway_state_1, metabolic_pathway_state_2, metabolic_pathway_state_3, observable_values, and 2 more.
The largest-observable-excursions chart ranks which reported variables moved the most during this simulation.
The phase-relationship plot compares paired observable values to show how the dominant trajectories move relative to one another.
Smallbone2013 - Metabolic Control Analysis - Example 2 Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network paramet. It can be used to explore metabolic flux dynamics and compare pathway behavior across conditions.
Runtime
Runs
Metadata
Manifest
{
"io": {
"inputs": [
{
"name": "initial_metabolic_pathway_state_1",
"units": "native SBML value",
"default": 0.05625738310526,
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.initial_metabolic_pathway_state_1",
"description": "Initial condition for metabolic pathway state 1. Maps to bundled SBML symbol `x1`. Applied before the Tellurium simulation starts; this does not change kinetic parameters or equations. Default from bundled SBML initial value."
},
{
"name": "initial_metabolic_pathway_state_2",
"units": "native SBML value",
"default": 0.76876151899652,
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.initial_metabolic_pathway_state_2",
"description": "Initial condition for metabolic pathway state 2. Maps to bundled SBML symbol `x2`. Applied before the Tellurium simulation starts; this does not change kinetic parameters or equations. Default from bundled SBML initial value."
},
{
"name": "initial_metabolic_pathway_state_3",
"units": "native SBML value",
"default": 4.23123848100348,
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.initial_metabolic_pathway_state_3",
"description": "Initial condition for metabolic pathway state 3. Maps to bundled SBML symbol `x3`. Applied before the Tellurium simulation starts; this does not change kinetic parameters or equations. Default from bundled SBML initial value."
}
],
"outputs": [
{
"name": "metabolic_pathway_state_1",
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.metabolic_pathway_state_1"
},
{
"name": "metabolic_pathway_state_2",
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.metabolic_pathway_state_2"
},
{
"name": "metabolic_pathway_state_3",
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.metabolic_pathway_state_3"
},
{
"name": "observable_values",
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.observable_values"
},
{
"name": "run_summary",
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.run_summary"
},
{
"name": "observable_labels",
"maps_to": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.observable_labels"
}
]
},
"title": "Smallbone2013 - Metabolic Control Analysis - Example 2 Lab",
"models": [
{
"path": "owned/models/metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model",
"alias": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model",
"parameters": {
"model_path": "data/BIOMD0000000455.xml",
"integration_step": 0.1
},
"provenance": {
"owned_path": "owned/models/metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model"
}
},
{
"path": "owned/models/visualisation",
"alias": "visualisation",
"provenance": {
"owned_path": "owned/models/visualisation"
}
}
],
"wiring": [
{
"to": [
"visualisation.metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model_observable_values"
],
"from": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.observable_values"
},
{
"to": [
"visualisation.metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model_run_summary"
],
"from": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.run_summary"
},
{
"to": [
"visualisation.metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model_observable_labels"
],
"from": "metabolism_sbml_smallbone2013_metabolic_control_analysis_example_biomd0000000455_model.observable_labels"
}
],
"runtime": {
"duration": 10,
"initial_inputs": {},
"communication_step": 1
},
"description": "Smallbone2013 - Metabolic Control Analysis - Example 2 Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network paramet. It can be used to explore metabolic flux dynamics and compare pathway behavior across conditions.",
"schema_version": "2.0"
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