PhysiCell Fibroblast Migration for Wound Healing

ISEF Category: Computational Biology and Bioinformatics

Subcategory: Computational Biomodeling  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Your skin heals faster when the cells can move across the wound like a crew laying bricks. In diabetic ulcers, that cell movement often slows down or stalls. Your project can model that process and ask which scaffold design gives fibroblasts the best path forward. That turns a hard medical problem into one you can test on a computer.

What Is It?

Fibroblasts are cells that help build and repair tissue. During wound healing, they migrate into the damaged area, make new support material, and help close the gap. If you picture a construction team crossing a broken bridge, fibroblasts are the workers trying to reach the site and rebuild it.

PhysiCell is a computer platform for simulating how cells behave in space and time. You can give cells rules for movement, sticking, growth, and response to the environment. Mechanical stiffness means how hard or soft a material feels to the cells. Porosity means how many open spaces the scaffold has. In this project, you test how those features change cell migration and whether one design helps fibroblasts spread more evenly across a wound.

Why This Is a Good Topic

This topic works well because you can change one input at a time, then measure a clear output like migration speed, coverage, or wound closure. It connects to a real medical need, since chronic diabetic ulcers affect healing and quality of life. You can also build strong computational skills, including model design, parameter testing, and data visualization, without needing a wet lab.

Research Questions

• How does scaffold stiffness change simulated fibroblast migration speed across a wound gap?
• What is the effect of scaffold porosity on the fraction of wound area covered by fibroblasts?
• Does combining higher porosity with moderate stiffness improve closure more than either factor alone?
• To what extent do different cell adhesion settings change the predicted healing pattern?
• Which stiffness range produces the most uniform fibroblast spread in the simulation?
• How does fibroblast density at the wound edge affect the time to predicted closure?

Basic Materials

• Laptop or desktop computer with enough memory to run simulation software.
• PhysiCell software and example models.
• Python for analysis and plotting.
• Jupyter Notebook or another notebook editor for tracking runs.
• Spreadsheet software for organizing parameter sets and output values.
• External storage or cloud backup for simulation files.
• Simple note template for recording each model setting and result.

Advanced Materials

• Workstation or university computer cluster access.
• PhysiCell source code and custom model files.
• Python libraries for data analysis and statistical testing.
• ImageJ or FIJI for measuring output images when needed.
• Version control software such as Git for tracking model changes.
• R or Python visualization packages for comparing many runs.
• Access to published wound-healing parameter values from biomedical papers.

Software & Tools

• PhysiCell: Simulates cell behavior in space and time for multicellular systems.
• Python: Organizes output, computes summary metrics, and makes graphs.
• Jupyter Notebook: Keeps code, notes, and figures in one place.
• ImageJ: Measures spatial coverage or image-based outputs when your model exports images.
• R: Runs statistical tests and makes comparison plots for multiple simulation conditions.

Experiment Steps

1. Define the biological question you want the model to answer and choose one outcome metric, such as migration distance or wound coverage.
2. Select the model parameters you will vary first, then decide which variables stay fixed so your results stay interpretable.
3. Build a baseline simulation that matches a simple wound-healing scenario before adding stiffness or porosity changes.
4. Plan a parameter sweep that covers low, medium, and high values for each scaffold feature.
5. Decide how you will compare runs, including summary statistics, graphs, and repeat simulations for randomness.
6. Prepare a validation plan that compares your simulated patterns with published wound-healing trends or accepted biological behavior.

Common Pitfalls

• Changing too many parameters at once, which makes it impossible to tell whether stiffness or porosity caused the result.
• Using only one simulation run per setting, which hides random variation in cell movement.
• Choosing output metrics that look nice but do not map to healing, such as raw cell count without spatial coverage.
• Ignoring boundary effects, which can make cells pile up at the model edge instead of behaving like they do in tissue.
• Comparing your simulation to real biology without checking whether the parameter values match published wound-healing data.

What Makes This Competitive

A strong version of this project does more than run a few sample simulations. You can test a full parameter sweep, compare multiple scaffold designs, and use careful statistics to show which differences really matter. You can also strengthen the work by validating the model against published fibroblast migration patterns and explaining where the simulation matches, or misses, biology. That kind of careful model testing stands out because it treats the computer model like a scientific tool, not a black box.

Project Variations

• Swap fibroblasts for keratinocytes and compare how the two cell types respond to the same scaffold settings.
• Test how oxygen or glucose stress changes migration predictions in a diabetic-wound version of the model.
• Add a second scaffold property, such as degradation rate, and study whether it changes the best porosity choice.

Learn More

• PhysiCell documentation: Read the official documentation and example models on the PhysiCell project site.
• PubMed: Search review articles on fibroblast migration, wound healing, and diabetic ulcers.
• NIH National Institute of Diabetes and Digestive and Kidney Diseases: Find background on diabetic wound complications and healing challenges.
• PubMed Central: Read free full-text papers on wound-healing models and scaffold design.
• MIT OpenCourseWare: Look for systems biology or computational modeling lectures that explain model building and analysis.
• NCBI Bookshelf: Search for free textbook chapters on wound repair and tissue regeneration.