Optimizing Phage Cocktails for E. coli

ISEF Category: Microbiology

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

The Hook

One bacteriophage can knock back a bacterial culture, but the culture can fight back fast. That makes phage therapy feel a little like whack-a-mole. Your job is to turn that chaos into a model, then test whether a 3-phage mix slows resistance better than a single phage. If you like math and microbes, this is a strong research problem.

What Is It?

Bacteriophages, or phages, are viruses that infect bacteria. Think of them like tiny key-and-lock tools. A phage can only infect a bacterium if the cell has the right surface features for that phage to grab onto. If the bacteria change those features, the phage may stop working.

A phage cocktail mixes several phages so the bacteria face more than one threat at once. The idea is simple. The bacteria can resist one phage, but resisting three at the same time should be harder. A Lotka-Volterra model helps you describe how two living populations change over time. Here, you can use it to predict how bacteria and phages interact, then ask which mix delays resistance the longest.

Why This Is a Good Topic

This topic works well because you can measure real outcomes, model them mathematically, and compare several strategies against the same bacterial strain. The question connects to phage therapy, food safety, and environmental microbiology, where people want ways to control bacteria without relying only on antibiotics. You can learn experimental design, curve fitting, and model validation, all in one project.

Research Questions

• How does phage identity affect the time to visible resistance in E. coli K-12?
• What is the effect of changing the starting ratio of three phages on bacterial regrowth after 72 h?
• Does a 3-phage cocktail suppress bacterial rebound more than the best single phage alone?
• To what extent does a Lotka-Volterra resistance-emergence model predict the observed kill curve for each phage treatment?
• Which phage combination produces the longest delay before resistant colonies appear?
• How does the order of phage addition change the final bacterial density?
• What is the effect of different multiplicities of infection on resistance emergence in a mixed phage treatment?

Basic Materials

• Safe BSL-1 E. coli K-12 culture from an approved source.
• Three well-characterized lytic phages that infect E. coli K-12.
• Sterile broth and agar plates approved by the supervising lab.
• Incubator with temperature control.
• Spectrophotometer or plate reader for turbidity readings.
• Micropipettes and sterile tips.
• Sterile culture tubes, flasks, and microcentrifuge tubes.
• Petri dish spreaders or plating tools.
• Digital colony counter or manual counting supplies.
• Lab notebook or spreadsheet for tracking growth curves.

Advanced Materials

• Phage stocks with known host range and titer.
• Multiple E. coli K-12 clones for cross-comparison.
• Automated plate reader with kinetic mode.
• Soft agar overlay supplies for plaque assays.
• Electron microscope access or sequencing data for phage confirmation, if available.
• PCR reagents for resistance marker checks.
• Computer with Python or R for model fitting.
• Statistical software for nonlinear regression and model comparison.
• Image analysis software for plaque or colony sizing.
• Biosafety-approved containment and waste handling supplies.

Software & Tools

• Python: Fits growth and kill-curve models, compares candidate phage schedules, and plots resistance trends.
• R: Runs nonlinear regression, confidence intervals, and statistical tests for treatment comparisons.
• ImageJ: Measures plaque size, colony density, or plate clearing from photographs.
• GraphPad Prism: Helps organize time-kill curves and compare treatment groups with common statistical tests.
• Jupyter Notebook: Keeps code, notes, and figures in one place for repeatable analysis.

Experiment Steps

1. Define the exact outcome you will optimize, such as delayed regrowth, lower final density, or fewer resistant colonies.
2. Choose three phages with different host-range patterns so each one adds a distinct selective pressure.
3. Collect single-phage time-kill data first so you can estimate how each phage changes bacterial growth over time.
4. Fit a resistance-emergence model to those single-phage curves and test whether one shared model or separate models fit better.
5. Compare candidate 3-phage mixtures in the model before you run the wet-lab validation, and rank them by predicted performance.
6. Plan a validation study that tests the top mixture against single-phage controls and records both killing and rebound over 72 h.

Common Pitfalls

• Using phages that all attack the same bacterial receptor, which makes the cocktail act like a single phage.
• Skipping single-phage baseline curves, which leaves you with no way to fit the resistance model.
• Treating optical density as the same thing as live cell count, which can hide resistant survivors.
• Mixing phages without checking their starting titers, which makes the comparison unfair.
• Fitting one curve to all treatments, which can hide that each phage may follow a different resistance pattern.

What Makes This Competitive

A strong version of this project does more than test whether a cocktail works. It explains why one mix works better, using model fit quality, confidence intervals, and fair comparisons against single-phage controls. You can raise the level by testing whether the model predicts a new phage schedule, not just the data you already used. Clear validation, careful statistics, and a thoughtful resistance metric will matter more than fancy equipment.

Project Variations

• Test the same phage-cocktail model on a different E. coli K-12 clone with known surface variation.
• Compare simultaneous phage mixing with staggered phage addition to see which delays rebound longer.
• Replace turbidity with plaque-based survival counts to see whether the model predicts viable survivors as well as growth curves.

Learn More

• PubMed: Search for review articles on phage therapy, phage resistance, and combination therapy in bacteria.
• NIH PubMed Central: Read free full-text papers on bacteriophage biology and experimental design.
• NCBI Bookshelf: Look for free microbiology and virology chapters that explain phage life cycles and bacterial growth.
• MIT OpenCourseWare: Search for biology, systems biology, or mathematical modeling courses that cover differential equations and population dynamics.
• ASM Journals: Search free abstracts and accessible articles on bacteriophage-host interactions and phage cocktails.
• CDC Biosafety in Microbiological and Biomedical Laboratories: Review basic lab safety guidance for work with BSL-1 organisms.