Food Dye Light Kill Curves in E. coli

ISEF Category: Microbiology

Subcategory: Antimicrobials and Antibiotics  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A dye can become a germ killer when you shine the right light on it. That makes this project feel like chemistry and microbiology teaming up. You will test whether cheap LED light and common dyes can knock down E. coli growth in a predictable way. If the pattern holds, you can turn simple kitchen-shelf molecules into a real antimicrobial study.

What Is It?

Photodynamic inactivation uses three things at once, a dye, light, and oxygen. The dye absorbs light and transfers that energy to oxygen around the cells. That creates reactive oxygen species, which are short-lived molecules that damage membranes, proteins, and DNA. In plain language, the dye acts like a tiny solar panel that helps make cell-damaging sparks.

Your job is to measure how much bacterial survival drops when you change the light dose. Light dose means how much light energy reaches the sample over time. The Bunsen-Roscoe reciprocity model says that, for some systems, the same total dose should give the same effect even if you change the mix of brightness and exposure time. Your project tests whether that rule holds for food dyes and E. coli K-12.

Why This Is a Good Topic

This is a strong science fair topic because you can test a clear cause-and-effect relationship. You change dye type, light dose, or exposure pattern, then measure colony-forming units, or CFU, to see how many bacteria survive. The project connects to antimicrobial resistance, low-cost disinfection, and light-based treatment ideas. You can also learn real lab skills, like experimental controls, plating, dilution logic, dose-response analysis, and model fitting.

Research Questions

• How does dye type affect CFU reduction in E. coli K-12 under the same 405 nm light dose?
• What is the effect of light dose on survival when curcumin, riboflavin, or methylene blue is held constant?
• Does the reciprocity model predict the same killing effect for equal total dose delivered with different light intensity and exposure time pairs?
• To what extent does a dark control change CFU counts compared with dye-plus-light samples?
• Which dye gives the steepest dose-response curve at low light doses?
• How does suspending bacteria in different simple media affect photodynamic killing efficiency?
• To what extent does the sample depth change the apparent light dose response?

Basic Materials

• E. coli K-12 strain and approved culture plates or broth medium.
• Curcumin, riboflavin, and methylene blue stock solutions prepared under approved lab rules.
• 405 nm LED light source with a stable power supply.
• Digital light meter or handheld irradiance meter for checking LED output.
• Sterile Petri dishes, culture tubes, and disposable inoculation tools.
• Micropipettes and sterile tips.
• Incubator set to the organism's approved growth conditions.
• Colony counter app or manual marker for CFU counting.
• Digital balance for preparing dye solutions.
• Amber foil or light-safe containers for dark controls.

Advanced Materials

• Spectroradiometer or calibrated photometer for mapping LED output across the exposure area.
• Biosafety cabinet or clean bench approved for bacterial handling.
• Autoclave for sterilizing media and waste.
• Plate shaker or vortex mixer for uniform sample mixing.
• UV-Vis spectrophotometer for checking dye absorbance before exposure.
• Optical filters or neutral density filters for matching light doses.
• Oxygen probe or dissolved oxygen meter for testing oxygen dependence.
• Image analysis setup for plate documentation and colony counting.
• Temperature probe to confirm that heating does not explain the kill effect.
• Statistical software with dose-response and regression tools.

Software & Tools

• ImageJ: Counts colonies from plate photos and helps compare treatment groups.
• R: Fits dose-response curves and tests whether light dose predicts survival.
• Python: Organizes raw CFU data, plots reciprocity curves, and checks model fit.
• JASP: Runs basic statistics without a heavy setup.
• Google Sheets: Tracks sample labels, exposure groups, and dilution calculations.

Experiment Steps

1. Define your core question, then choose one dye, one bacterial strain, and one light wavelength as your starting point.
2. Map the treatment groups, including dark controls, dye-only controls, light-only controls, and combined dye-plus-light samples.
3. Decide how you will express dose, so you can compare intensity, exposure time, and total energy on the same scale.
4. Plan a plating and dilution scheme that gives countable colonies across all treatment levels.
5. Build a calibration plan for light output, dye absorbance, and sample geometry so your dose estimate stays consistent.
6. Choose the analysis model before collecting data, then test whether the reciprocity rule fits better than a simple threshold model.

Common Pitfalls

• Using room light during setup, which can pre-activate photosensitive dyes and blur your controls.
• Letting the LED beam fall unevenly across the plate, which makes some samples get more dose than others.
• Skipping dye-only controls, which makes it impossible to tell whether killing came from the dye or the light.
• Counting plates with too many or too few colonies, which wrecks CFU estimates and hides real trends.
• Ignoring oxygen or temperature changes during exposure, which can make the project look like a light study when the real driver is something else.

What Makes This Competitive

A stronger project does more than show that light plus dye reduces growth. It tests a real model, checks whether equal dose means equal killing, and compares several dyes under the same conditions. You can also improve the work by adding careful calibration, stronger controls, and statistics that compare competing fits instead of just comparing averages. That gives your data a real scientific story instead of a simple demo.

Project Variations

• Test the same photodynamic approach on a Gram-positive bacterium to compare cell wall effects.
• Swap CFU counting for live-dead staining and image analysis to compare two readouts of cell damage.
• Compare continuous light exposure with pulsed light at the same total dose to see whether timing changes killing.

Learn More

• PubMed: Search for review articles on photodynamic antimicrobial therapy, E. coli, and visible-light photosensitizers.
• NIH PubMed Central: Read full-text papers on bacterial photoinactivation and reactive oxygen species.
• NOAA National Center for Biotechnology Information resources: Search NCBI Bookshelf and PubMed for basic microbiology and light damage background.
• Antimicrobial Agents and Chemotherapy: Search the journal for studies on photodynamic killing and dose-response methods.
• Frontiers in Microbiology: Search for open-access reviews on antimicrobial photodynamic therapy and bacterial stress responses.
• MIT OpenCourseWare: Use microbiology and biochemistry materials to review experimental design, statistics, and data interpretation.