CompuCell3D Workflows: From Model Design to VisualizationCompuCell3D is an open‑source modeling environment for simulating and visualizing multicellular systems. It combines a cell‑based modeling framework (the Cellular Potts Model) with reaction‑diffusion solvers, deformable cell mechanics, and extensive scripting capabilities. This article walks through a practical workflow: defining a biological question, designing a model, implementing it in CompuCell3D, running simulations, analyzing results, and creating publication‑quality visualizations. Examples and practical tips are provided at each stage to help newcomers and intermediate users get reproducible, robust results.
1. Framing the biological question
A clear biological question guides model scope, scale, and complexity. Example questions include:
- How do chemotactic gradients guide collective cell migration during wound healing?
- What role does differential adhesion play in tissue sorting and boundary formation?
- How do coupled reaction‑diffusion processes produce spatial patterning (Turing patterns) in a growing tissue?
Decide early whether you need single‑cell resolution, subcellular signaling, mechanical deformation, or coupling to extracellular matrices. Choose measurable outputs (e.g., cell shape, neighbor counts, concentration profiles, pattern wavelength) and experimental datasets for validation if available.
2. Choosing model components
CompuCell3D offers modular components that you combine depending on your question:
- Cellular Potts Model (CPM) for individual cell behaviors: adhesion, volume/surface constraints, cell types, motility, chemotaxis.
- Reaction‑Diffusion solvers for extracellular and intracellular chemical fields.
- Steppable and Python scripting for custom rules, parameter sweeps, data logging, and dynamic changes (e.g., growth, division).
- Plugin mechanics for cell‑cell and cell‑matrix interactions, polarization, and extracellular matrix (ECM) modeling.
Balance biological realism and computational cost. Start with a minimal model that captures the phenomenon and incrementally add features.
3. Model design: parameters and initial conditions
Define:
- Domain size and geometry: 2D vs 3D, lattice resolution (pixels/voxels per micron).
- Cell types and states: number, initial positions, sizes, and type‑specific parameters (target volume, adhesion energies).
- Chemical fields: initial concentrations, sources/sinks, diffusion coefficients, decay rates.
- Motility and mechanics: temperature (lambda_motility), chemotactic strengths, adhesion matrix J values, volume/surface constraint weights.
Practical tip: nondimensionalize or scale parameters to your lattice units. Keep a parameter table for reproducibility.
4. Implementing the model in CompuCell3D
CompuCell3D models are typically organized into three files:
- XML (CC3DML) file: defines lattice, cell types, energy terms, diffusion fields, and plugin parameters.
- Python scripting file: contains Steppables for initialization, custom rules, data collection, and runtime control.
- Optional SBML file: for detailed biochemical networks (can be linked to cells or the medium).
Example structure (files):
- MyModel.cc3d (wrapper that references the XML and Python)
- MyModel.xml (CC3DML)
- steppables.py (initialization and runtime behavior)
- params.py (parameter definitions)
- sbml_models/*.xml (SBML networks)
Use the built‑in Player for local runs, or run headless via cc3d command line for batch/cluster jobs.
5. Example: chemotactic wound‑healing model (outline)
Model goal: simulate collective migration of epithelial cells toward a chemoattractant released at a wound site.
Key components:
- Cell types: “Epithelium” and “Medium”
- CPM settings: target volume, lambdaVolume, adhesion J(Epithelium, Medium)
- Chemical field: chemoattractant diffusing from a localized source with decay
- Steppable: wound creation (remove cells in a region), chemotaxis plugin toward gradient, cell proliferation/leader cell behavior
Initialization pseudocode (in steppables.py):
# fenced code example for initialization from cc3d import CompuCellSetup class InitSteppable(SteppableBasePy): def start(self): # place epithelial sheet, define wound region, initialize chemo field pass Tune parameters iteratively: too strong adhesion prevents migration; too weak adhesion leads to cell scattering.
6. Running simulations: strategies and practicalities
- Start with short test runs (few hundred MCS) to check initialization and basic behaviors.
- Use parameter scans to explore sensitivity: vary chemotaxis strength, diffusion coefficient, or adhesion J systematically.
- For reproducibility, fix random seeds when comparing parameter sets.
- For computationally heavy 3D simulations, run headless on a cluster and use periodic checkpointing to save state.
Performance tips:
- Reduce lattice resolution if fine detail isn’t necessary.
- Limit the number of tracked fields; each adds computation.
- Profile Python Steppables — move heavy computations into compiled code or optimize algorithms.
7. Data collection and analysis
Collect:
- Cell‑level data: positions, volumes, shapes, neighbor lists, type transitions (log in steppables).
- Field data: concentration maps at time points (save as TIFF/VTK).
- Aggregate metrics: migration speed, wound closure fraction, pattern wavelength.
Common analysis tools:
- Python (NumPy/Pandas/Matplotlib) for time series and statistics.
- ImageJ/Fiji for image‑based measurements.
- ParaView for 3D field visualization and volumetric data.
Example data logging snippet:
# fenced code example for logging cell positions with open('cell_positions.csv','w') as f: f.write('mcs,cell_id,x,y,type ') # inside step(): for cell in self.cellList: f.write(f"{mcs},{cell.id},{cell.xCM},{cell.yCM},{cell.type} ") 8. Visualization and figure preparation
CompuCell3D Player offers interactive visualization: overlay cells and chemical fields, change colormaps, and record movies. For publication figures:
- Export high‑resolution frames (PNG/TIFF) or VTK files for 3D rendering.
- Combine overlays: cell outlines plus heatmap of chemical concentration.
- Use consistent color schemes and scale bars. Annotate time points and parameter values.
- For movies, use ffmpeg to combine frames and add annotations.
Example ffmpeg command to make a movie:
ffmpeg -framerate 10 -i frame_%04d.png -c:v libx264 -pix_fmt yuv420p -crf 18 output.mp4 9. Validation and parameter inference
- Compare simulated outputs to experimental data (shape distributions, closure rates, concentration profiles).
- Use sensitivity analysis and parameter sweeps to identify influential parameters.
- For parameter fitting, consider approximate Bayesian computation (ABC) or optimization libraries that call CompuCell3D headless runs.
10. Best practices and reproducibility
- Keep code and parameters under version control (Git).
- Document model assumptions, parameter units, and nondimensionalization.
- Share complete model packages (XML, Python, SBML, example data) alongside manuscripts.
- Use checkpoints and logs for long runs; store random seeds and software versions.
11. Advanced topics (brief)
- Coupling to finite‑element mechanical models or agent‑based frameworks.
- Multi‑scale linking: subcellular SBML networks controlling cell fate decisions.
- GPU acceleration and parallelization strategies for large 3D domains.
- Hybrid models combining continuum tissue mechanics with discrete CPM cells.
12. Resources and community
CompuCell3D has active mailing lists, tutorials, and example models. Start from the official model gallery and step through tutorial notebooks to learn workflows. Engage with the community for troubleshooting and sharing models.
Horizontal rule
This workflow—question → components → parameters → implementation → simulation → analysis → visualization—provides a structured path from biological idea to interpretable model outputs using CompuCell3D.
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