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Model
Source and simulation metadata
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This public demo shows mechanistic model outputs for research exploration only. It is not medical advice, clinical decision support, diagnosis, prognosis, or a treatment recommendation. Do not enter personal health information.
Current Model
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Set timing and assumptions below, run the model, then inspect the result manifest and plots.
Simulation
Run one baseline or edited-assumption trajectory.
- Choose a time span and edit assumptions if needed.
- Run the bounded window or the accelerated full model.
- Check the run summary, inspect separate-scale plots, and download the data.
Inputs
Use minutes for exact Stella timing, or set life-span years for age-scale projections.
Life Span
Life-scale timing will appear after a model loads.
Assumptions
Edit source constants here. Changes are applied only when you run; validation evidence always uses the unmodified Stella source.
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Run
Bounded runs are for short windows; full runs use the registered accelerated provider.
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Results
Review the active run, load a saved JSON run, or export the plotted series.
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Simulation Plots
Research Projection
Final 2026 biomarker-based research scenario.
- Enter de-identified biomarker inputs and explicit treatment assumptions.
- Run a comparison or compare the predefined model scenarios.
- Interpret the trajectories as exploratory model output, not clinical advice.
Results
Compare predicted DAv trajectories, threshold ages, and modeled scenario comparisons.
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DAv trajectory
nmol
DAv percent of control
percent
Treatment Comparison
Batch Sweeps
Compare kMAO_DA reduction and treatment-timing families.
- Choose the source or de-identified research basis and define the scenario ranges.
- Run the independent curves; completed reports are saved automatically.
- Confirm every sanity check before interpreting plots or downloading artifacts.
Results
Inspect saved artifacts, sanity checks, comparison curves, and threshold-delay metrics.
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Saved Sweeps
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kMAO_DA Reduction Family
DAv nmol
Treatment Start-Age Family
DAv nmol
| Family | Curve | Start age | Decrease | Threshold age | Delay |
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Validation
Separate source fidelity evidence from exploratory scenario output.
- Run the registered checks for the selected model.
- Read the validation grade and per-variable evidence before using results.
- Upload a Stella CSV below for an independent reference comparison.
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ISDB Compatibility
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Equation Residuals
Checking saved values against equations...
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Constrained Flow Residuals
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Pass-Through Inference
Searching stock pass-through candidates...
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Full-Run Saved-Series Validation
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Overlay
Residuals
Stella CSV Reference
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Overlay
Residuals
Generated Runtime Validation
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About The Model
Research context, provenance, and source articles.
- Read the research purpose and authorship.
- Open the source articles for the biological and modeling rationale.
- Review interpretation and privacy limits before entering any inputs.
Who Dr. Goldstein Is
Dr. David S. Goldstein is the author of the putamen dopamine modeling work reproduced here. The primary 2026 model article lists his affiliation as the Clinical Neurosciences Program, Division of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health. His research program has focused on catecholamine biology, autonomic/neurocardiology disorders, and dopamine-related mechanisms in Parkinson's disease.
What This Tool Does
This browser tool translates copied Stella Architect putamen dopamine dynamics models into Python, then lets users inspect equations, run bounded simulations, compare validation evidence, and explore research scenarios such as kMAO_DA reduction sweeps. The goal is transparency and reproducibility: every plotted curve should be traceable to the Stella source model, the Python execution path, and the published mechanistic assumptions.
Research Articles
Predictive modeling of putamen dopamine in Parkinson's disease
Primary 2026 AJP-Regulatory model article.
Putamen dopamine synthesis, vesicular storage, and metabolism in Parkinson disease
2026 kinetic-analysis layer used for biomarker-to-parameter context.
The catecholaldehyde hypothesis: where MAO fits in
Review article motivating MAO, DOPAL, and oxidative-stress mechanisms.
Comparison of Monoamine Oxidase Inhibitors in Decreasing Production of DOPAL in PC12 Cells
Experimental context for MAO inhibition and DOPAL production.
Interpretation
Scenario curves are model projections from published equations and copied Stella source files. They are investigational outputs, not patient-specific medical conclusions.
Privacy
Do not enter names, dates of birth, medical record identifiers, contact information, or other personal health information. Inputs should be synthetic or de-identified research examples.
Model Details
Inspect provenance, equations, wiring, and generated execution source.
- Confirm the source simulation settings and variable equations.
- Use the wiring tables to inspect every stock, flow, and dependency.
- Review generated Python as implementation evidence, not a separate model.
Simulation Specs
Variables
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Stock-Flow Diagram
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Flow Wiring
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Dependency Connectors
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Generated Python Source
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