B. subtilis Swarming and Pattern Formation Study

ISEF Category: Microbiology

Subcategory: Bacteriology  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A tiny change in surface stiffness can flip a bacterial colony from smooth spread to wild branching. That is a phase change, and you can measure it. If you like patterns that look a little like lightning, rivers, or crystals, this project has that energy.

What Is It?

Swarming motility is how some bacteria move together across a surface in a coordinated group. B. subtilis is a common model for this behavior. Instead of one cell crawling alone, thousands of cells act like a crowd, and the colony can spread in rings, waves, or branching arms.

Think of the colony like a crowd on a dance floor. If the floor is easy to move across, the crowd spreads smoothly. If the floor gets stiffer, the movement changes and the pattern can break into branches. Your job is to test when that switch happens and whether a reaction-diffusion model, like Keller-Segel, can predict the change. In this model, cells move, grow, and respond to chemicals they and their neighbors make. The math helps explain why a colony forms a pattern instead of a simple circle.

Why This Is a Good Topic

This is a strong science fair topic because you can change one main variable, then measure a clear outcome. Agar concentration changes surface stiffness, and nutrient gradients change how strongly cells move and grow, so the system gives you real cause-and-effect data. You can also collect images, quantify pattern shape, and compare your results to a mathematical model, which adds depth beyond a simple observation project.

Research Questions

• How does agar concentration change the branching pattern of B. subtilis swarms?
• What is the effect of a nutrient gradient on the speed of colony expansion?
• Does increasing agar stiffness shift the colony from smooth radial spread to branching growth?
• To what extent can a Keller-Segel model predict the measured swarm front over time?
• Which agar concentration produces the sharpest transition in pattern shape?
• How does nutrient level affect the number of branches formed at the colony edge?
• What is the effect of starting inoculum size on the time needed to reach a pattern transition?

Basic Materials

• Bacillus subtilis strain from a school or university lab culture collection.
• Sterile petri dishes.
• Prepared agar plates at several concentrations.
• Nutrient broth or other approved growth medium.
• Sterile inoculating loops or disposable spreaders.
• Micropipettes and sterile tips.
• Parafilm or plate sealing film.
• Digital camera or phone camera with a tripod.
• Ruler or printed scale for image calibration.
• Lab notebook.
• PPE, including gloves, goggles, and a lab coat.

Advanced Materials

• Bacillus subtilis strain from a university teaching or research lab.
• Incubator with stable temperature control.
• Prepared agar plates with carefully graded concentrations.
• Sterile media for creating nutrient gradients.
• Stereomicroscope or macro imaging setup.
• Motorized stage or fixed time-lapse imaging platform.
• Image analysis workstation.
• Reagents for tracking biomass or cell density if your lab uses them.
• Agar rheology or gel stiffness measurement tools, if available.
• Statistical analysis software.
• Access to biosafety-approved microbiology workspace.

Software & Tools

• ImageJ: Measures colony area, edge shape, branch count, and front position from time-lapse images.
• Python: Fits growth curves, extracts motion features, and compares model predictions with measured patterns.
• Fiji: Simplifies frame-by-frame image processing for colony outlines and texture analysis.
• R: Runs statistics, plots phase transitions, and tests whether agar concentration changes pattern class.
• GeoGebra: Helps you sketch model behavior and check how parameter changes affect predicted fronts.

Experiment Steps

1. Define the exact pattern feature you will measure, such as front speed, branch count, or transition point.
2. Choose one variable to change first, such as agar concentration, while holding the nutrient recipe and inoculum size constant.
3. Plan a time-lapse imaging setup that keeps lighting, distance, and framing fixed across all plates.
4. Build an analysis plan that turns colony images into numbers before you collect data, so your measurements stay consistent.
5. Select a mathematical model and identify which parameters you will estimate from your own data.
6. Decide how you will test for a phase transition, such as comparing pattern classes across a range of agar stiffness values.

Common Pitfalls

• Changing the lighting between imaging sessions, which makes the colony edge look different even when growth has not changed.
• Using plates with slightly different agar thickness, which confounds stiffness with depth.
• Measuring only final colony size, which misses the branching transition you actually want to model.
• Mixing up nutrient effects with stiffness effects by changing both at the same time.
• Fitting a reaction-diffusion model to noisy images before cleaning up the image scale and time stamps.

What Makes This Competitive

A strong project goes beyond pretty colony pictures. You need clean controls, a narrow hypothesis, and a quantitative way to define the transition point. A more competitive version compares real swarm data against model output, tests whether the threshold shifts with nutrient gradient strength, and uses statistics to decide whether the pattern change is real or just visual noise. That kind of analysis shows you understand both biology and modeling.

Project Variations

• Test how different carbon sources change the branching threshold in B. subtilis swarms.
• Compare swarming behavior on agar plates with different salt levels to see whether osmotic stress shifts the phase transition.
• Use image texture analysis instead of branch counting to quantify when the colony changes from smooth to dendritic growth.

Learn More

• PubMed: Search review articles on Bacillus subtilis swarming motility, pattern formation, and chemotaxis.
• NIH NCBI Bookshelf: Find free background chapters on microbial growth, signaling, and systems biology.
• NCBI PubMed Central: Read full-text papers on bacterial pattern formation and reaction-diffusion models.
• MIT OpenCourseWare: Search for systems biology or mathematical biology materials that explain differential equations in living systems.
• Nature Reviews Microbiology: Search for review articles on bacterial collective behavior and swarming.
• NASA Image Analysis resources: Use general image-processing tutorials for time-lapse measurement ideas and workflow planning.