Slime Mold Network Models for Vascular Growth

ISEF Category: Biomedical Engineering

Subcategory: Cell and Tissue Engineering  ·  Difficulty: Intermediate  ·  Setup: School Lab  ·  Time: 1 to 2 Months

The Hook

A slime mold can solve a maze without a brain. That sounds impossible, but Physarum polycephalum can build efficient transport networks as it searches for food. You can study the same design rules that help blood vessels grow and spread. This makes it a strong bridge between living systems, imaging, and biomedical engineering.

What Is It?

Physarum polycephalum is a single-celled organism that spreads across a surface and forms vein-like tubes. When food sits in different spots, the slime mold changes its shape and flow paths to reach the best reward. Think of it like a living road network that keeps redrawing itself as the map changes.

Researchers like Physarum because its network is easy to see and measure. You can photograph the tubes, turn the image into a skeleton, which is a thin line version of the network, and then measure branch lengths, angles, and widths. Those measurements let you compare the slime mold’s choices with simple design rules, like Murray’s law, which links vessel size to flow efficiency, and with real vascular patterns from biology papers.

Why This Is a Good Topic

This is a good science fair topic because you can change one food layout or nutrient pattern, then measure how the network responds. You do not need a medical lab to start, but you still get to work with real biological design ideas from blood vessels and tumors. The project teaches image analysis, graph thinking, and fair testing, which are useful skills in biomedical engineering.

Research Questions

• How does the spacing of nutrient sources affect the total length of the Physarum network?
• How does the number of food nodes change branch point density in the final network?
• What is the effect of nutrient gradient strength on tube thickness distribution?
• To what extent do Physarum branch widths follow Murray’s law predictions?
• Which food arrangement produces the most looped network versus the most tree-like network?
• Does the skeletonized network topology of Physarum more closely match healthy vessel datasets or tumor-vasculature datasets?

Basic Materials

• Physarum polycephalum culture from a mail-order supplier or classroom source.
• Oat flakes or another standard Physarum food source.
• Non-nutrient agar plates or petri dishes.
• Clear plastic containers or petri dishes with lids.
• Distilled water.
• Forceps or clean tweezers.
• Digital kitchen scale with 0.1 g accuracy.
• Ruler or calipers.
• Smartphone camera with a fixed stand.
• Dark background board for photos.
• Latex-free gloves.
• Notebook for mapping each trial.
• Marker for labeling plates.

Advanced Materials

• Stereo microscope or digital microscope.
• Flatbed scanner for high-contrast imaging.
• Micropipettes and tips for controlled nutrient placement.
• Image calibration slide or ruler.
• Thin-section graph paper or printed templates for grid layouts.
• Computer with ImageJ installed.
• Computer with Python and NetworkX.
• Access to published tumor-vasculature image sets or datasets from peer-reviewed papers.
• Access to a school or university lab incubator if temperature control is needed.
• Optional humidity chamber for consistent growth conditions.

Software & Tools

• ImageJ: Measures tube width, network area, and branch geometry from images.
• Python: Organizes measurements and runs custom analysis on network structure.
• NetworkX: Calculates graph metrics such as degree, path length, and clustering.
• Google Sheets: Tracks trial conditions and compares basic summary statistics.
• PubMed: Finds review papers and primary studies on vascular patterning and tumor networks.

Experiment Steps

1. Define the network feature you will measure first, such as total length, branch count, or tube width.
2. Choose one nutrient layout variable to change, such as spacing, number of nodes, or arrangement shape.
3. Plan a photo setup that keeps scale, lighting, and camera angle the same for every trial.
4. Build an analysis workflow that turns each image into a skeleton and a set of numeric network features.
5. Select comparison rules from vascular biology, then decide how you will test whether the slime mold data matches them.
6. Predefine controls, replicates, and exclusion rules so you can defend your results later.

Common Pitfalls

• Using changing light or camera distance, which makes the skeletonized network look different even when the organism did not change much.
• Letting plates dry out unevenly, which can push Physarum to grow in odd directions that do not reflect the nutrient pattern.
• Comparing only one dramatic image instead of replicates, which makes the network result look stronger than it really is.
• Mixing up live growth patterns with leftover slime mold trails from older trials, which can corrupt topology measurements.
• Treating a photo trace as a finished analysis, which can hide errors in branch width, node detection, and scale conversion.

What Makes This Competitive

A stronger project goes past pretty pictures and into clean measurement. You can compare several nutrient layouts, then test whether the network obeys a graph rule, a scaling law, or a vessel model with real statistics. A competitive version also explains why the slime mold matches some vascular features better than others, and where tumor data breaks the pattern. That kind of comparison shows design thinking, not just observation.

Project Variations

• Compare slime mold networks grown from circular food layouts versus linear food layouts.
• Test whether different nutrient types change tube thickness and branch stability.
• Compare Physarum skeleton graphs with published healthy vessel and tumor vessel image sets using the same metrics.

Learn More

• PubMed: Search for review articles on Physarum polycephalum, vascular network formation, and Murray’s law.
• NIH PubMed Central: Find full-text biology and bioengineering papers that you can read without a subscription.
• NOAA National Centers for Environmental Information: Use for background on network pattern analysis methods and data handling examples when you need a free research model.
• ImageJ documentation: Learn how to threshold images, skeletonize networks, and measure branch properties.
• MIT OpenCourseWare: Search for systems biology, transport networks, or biological engineering courses that explain network analysis tools.