viva-tumor-Vivarium-2
A process-bigraph port of the multiscale agent-based tumor microenvironment model, including tumor, T-cell, and dendritic-cell biology, diffusing IFNg and tumor-debris fields, and cell-cell neighbor exchange.
How this model is classified mathematically and dynamically.
- Model class
- Formalism
- Determinism
- Stochastic
- Time dynamics
- Discrete
- Spatial
- 2D
- Multiscale
- Yes
Organisms, entities and processes this model represents.
- Organisms
- Not recorded
- Infectious agents
- Not recorded
- Health conditions
- Not recorded
- Biological processes
- Molecular entities
- Not recorded
- Proteins / genes
- Not recorded
The data this model consumes and produces when executed.
Parameters
| Name | Default | Unit | Meaning |
|---|---|---|---|
| n_tumors | 60 | molecule count | Number of tumor cells |
| n_tcells | 6 | molecule count | Number of T cells |
| pd1_positive_frac | 0.25 | Not recorded | Starting fraction of PD1-positive T cells |
| tumor_pdl1n_frac | 0.9 | Not recorded | Starting fraction of PDL1n tumor cells |
| bounds | [400,400] | micrometer | Two-dimensional chamber bounds |
| n_bins | [40,40] | Not recorded | Number of bins in each field dimension |
| depth | 15 | micrometer | Field depth |
| interval | 60 | second | Simulation tick interval |
| IFNg_threshold | 15000 | molecule count | Internalized IFNg threshold for PDL1n to PDL1p conversion |
| activation_time | 21600 | second | TCR activation time |
| PD1n_IFNg_production | 4.5 | nanogram per milliliter | IFNg production rate for active T cells |
Initial conditions
| Name | Value | Unit |
|---|---|---|
| cells | tumor, T-cell, and optional dendritic-cell agents | Not recorded |
| fields.IFNg | zero 2D array | nanogram per milliliter |
Outputs
| Name | Quantity | Unit | Format |
|---|---|---|---|
| population_timeseries | population count | Not recorded | JSON |
| IFNg_field | concentration | nanogram per milliliter | Not recorded |
| cell_trajectories | spatial specification | micrometer | HTML |
| study_results | Study verdicts, summaries, and persisted run metadata | Not recorded | JSON |
Experiment protocol
Vivarium/process-bigraph simulations advance a two-dimensional agent-based composite in 60-second ticks; studies compare no-T, 25% PD1+, and 75% PD1+ conditions and replicate seeds.
- Timestep
- 60 {'value': 'second', 'iri': 'http://purl.obolibrary.org/obo/UO_0000010', 'ontology_label': 'second', 'ontology': 'uo', 'mapping_confidence': 'high'}
- Duration
- 600 {'value': 'second', 'iri': 'http://purl.obolibrary.org/obo/UO_0000010', 'ontology_label': 'second', 'ontology': 'uo', 'mapping_confidence': 'high'}
- Observables
- tumor populationT-cell populationPDL1p fractionPD1p fractionIFNg maximumdeathsdivisions
Runtime environment, dependencies, and compute requirements.
- Language
- Python >=3.11
- Environment
- native
Install the sibling viva-munk checkout before editable installation; studies write JSON, HTML, and visualization artifacts.
Compute requirements
- GPU
- Not required
- Parallelism
- single
Dependencies
- runtime
- numpy
- scipy
- process-bigraph >=1.8.5
- bigraph-schema >=0.0.60
- viva-munk
- plotly
- optional
- pytest
- matplotlib
Containers
- docker · docker.io/mismplatform/envbuild@sha256:6d25d1034d04791169f3b5e4a2b4ff4304cb8968d20969548cb43ddc5020c09f · docker.io
Image approved · envbuild-agent · Oct 2026
Entry points
python scripts/persist_study_runs.pyPersist one representative run per study
python scripts/scale_efficacy.py <N_TUMORS> <N_TCELLS> <STEPS> <BOUNDS> <NBINS> <NSEEDS>Run a larger-scale efficacy probe and write scalar observables
- N_TUMORS
- N_TCELLS
- STEPS
- BOUNDS
- NBINS
- NSEEDS
python scripts/regen_spatial_gifs.py <which>Regenerate spatial GIF/HTML figures
- which
python studies/tumor-microenvironment/sims/run.pyRun the tumor microenvironment headline experiment
python studies/efficacy-at-scale/sims/run.pyRun the efficacy-at-scale study
python studies/phenotype-conversion/sims/run.pyRun the phenotype-conversion study
python studies/tcell-exhaustion/sims/run.pyRun the T-cell exhaustion study
python studies/killing-assay-cytotoxicity/sims/run.pyRun the killing-assay cytotoxicity study
Tests
pytestpytest -q
Artifacts stored with this model.
Identifiers and integrity.
- Metadata review
- Approved · 1da614bf-946a-4400-a0be-4fd0f1d00f74 · Oct 2026
- Contacts
- Eran Agmon · agmon.eran@gmail.com
- External IDs
- url:https://github.com/vivarium-collective/viva-tumor-tcell