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🤖 Using Tissue Simulator with Large Language Models

Quick Start

The Tissue Simulator can be used as a tool by Large Language Models (LLMs) through the Model Context Protocol (MCP).

1. Install MCP Support

pip install mcp

2. Configure Claude Desktop

Edit your Claude Desktop config file and add:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "tissue-simulator": {
      "command": "python",
      "args": ["/Users/cramere/tissue_simulator/run_mcp_server.py"]
    }
  }
}

3. Restart Claude Desktop

The tissue simulator tools will now be available!

Example Conversations

Generate and Analyze Tissue

You: Can you create a simulated tissue section with epithelial and stromal cells?

Claude: I'll create a tissue simulation for you.

[Uses create_tissue tool]
[Uses generate_cells tool]
[Uses get_tissue_statistics tool]

I've created a 400x400x100 μm tissue with:
- 152 epithelial cells (radius: 6-10 μm)
- 82 stromal cells (radius: 8-15 μm)
- Packing fraction: 0.312
- 36 boundary cells

Would you like me to create visualizations or export the data?

Create Serial Sections

You: Create 5 serial sections through the tissue and analyze the cell distribution.

Claude: I'll create serial sections and analyze them.

[Uses create_serial_slices tool]
[Uses get_slice_statistics for each]

Created 5 sections:
- Section 1 (z=16.7 μm): 45 cells
- Section 2 (z=33.3 μm): 68 cells
- Section 3 (z=50.0 μm): 89 cells (peak)
- Section 4 (z=66.7 μm): 72 cells
- Section 5 (z=83.3 μm): 51 cells

The cell density peaks in the middle section, which is typical
for uniform cell distributions.

Export and Visualize

You: Export the data and create visualizations.

Claude: I'll export the data and generate visualizations.

[Uses export_tissue_csv tool]
[Uses export_slice_csv tool]
[Uses visualize_tissue tool]
[Uses visualize_slice_2d tool]

Data exported to:
- /tmp/tissue_sim_xyz/tissue_data.csv (234 cells)
- /tmp/tissue_sim_xyz/slice_data.csv (89 cells)

Visualizations created:
- /tmp/tissue_sim_xyz/tissue_3d.png
- /tmp/tissue_sim_xyz/slice_2d.png

You can download these files from the paths shown.

Available Tools

  1. create_tissue - Define tissue dimensions and cell types
  2. generate_cells - Populate with cells using sphere packing
  3. get_tissue_statistics - Analyze tissue composition
  4. create_slice - Extract 2D slice at any angle
  5. get_slice_statistics - Analyze slice composition
  6. create_serial_slices - Create multiple parallel slices
  7. export_tissue_csv - Export 3D tissue data
  8. export_slice_csv - Export 2D slice data
  9. visualize_tissue - Generate 3D visualization
  10. visualize_slice_2d - Generate 2D visualization
  11. reset_tissue - Start fresh simulation

Use Cases with LLMs

1. Exploratory Analysis

"Generate tissues with different cell type ratios and compare 
their packing efficiencies."

The LLM will: - Create multiple tissues - Generate cells with different parameters - Compare statistics - Provide insights

2. Histology Simulation

"Create a tissue and generate serial sections like in real 
histology. Analyze how cell counts vary across sections."

The LLM will: - Create tissue - Generate serial sections - Analyze each section - Identify patterns

3. Parameter Optimization

"Find the optimal max_attempts parameter to achieve a packing 
fraction of at least 0.35."

The LLM will: - Try different parameters - Generate and analyze tissues - Converge on optimal value - Explain the results

4. Educational Demonstrations

"Explain how tissue slicing works and demonstrate it with 
visualizations at different angles."

The LLM will: - Create tissue - Make slices at various angles - Generate visualizations - Explain the geometry

Advanced Usage

Custom Workflows

The LLM can create sophisticated workflows like:

# Pseudo-code of what the LLM might do:
1. create_tissue(specific parameters)
2. generate_cells(optimized parameters)
3. create_serial_slices(10 slices)
4. for each slice:
     analyze statistics
     export data
5. compare all slices
6. generate visualizations
7. provide comprehensive report

Comparative Studies

"Compare tissues with 2 cell types vs 4 cell types. Which has 
better packing efficiency?"

The LLM will design and execute a comparative study.

Data Analysis

"Analyze how cell density varies with depth in the tissue."

The LLM will: - Create serial sections - Extract statistics - Identify trends - Provide analysis

Tips for LLM Interactions

Be Specific

Avoid vague prompts like "Make a tissue".

Prefer concrete prompts like "Create a 500x500x100 μm tissue with epithelial cells (7-11 μm) and stromal cells (9-15 μm)".

Sequential Requests

The tools maintain state, so you can build on previous actions:

1. "Create a tissue with 3 cell types"
2. "Now create a slice at z=50"
3. "Export that slice data"
4. "Create another slice at 45 degrees"

Ask for Analysis

The LLM can interpret the results:

"What does a packing fraction of 0.35 mean? Is that good?"
"Why are there fewer cells in the edge slices?"
"How does the min_spacing parameter affect the results?"

Troubleshooting

Tools Not Showing Up

  1. Check MCP is installed: pip install mcp
  2. Verify config file location
  3. Check JSON syntax in config
  4. Restart Claude Desktop
  5. Look for error messages

Server Not Running

  1. Test manually: python run_mcp_server.py
  2. Check Python path in config
  3. Verify package is installed: pip install -e .

Unexpected Results

  1. Use reset_tissue to start fresh
  2. Check parameter ranges
  3. Ask the LLM to explain what happened

Performance Notes

  • Tissue generation: ~10-30 seconds for medium tissues
  • Slicing: Near-instant (milliseconds)
  • Visualization: ~2-5 seconds
  • CSV export: Fast for thousands of cells

Security Notes

  • MCP server runs locally
  • No network access required
  • Files stored in temp directory
  • Cleaned on server restart

Learn More

Examples Repository

More example conversations and workflows: examples/mcp_examples/

Now you can use natural language to create and analyze tissue simulations! 🧬🤖