Yeast Water Sensor for Estrogen Disruptors

ISEF Category: Biomedical Engineering

Subcategory: Synthetic Biology  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Tiny chemical signals can hide in plain sight. A yeast cell can act like a living light switch, turning color when it senses a target molecule. That idea powers real biosensor research, and you can model the logic without needing a fancy lab. Your project can ask how well a yeast-based sensor might flag estrogen-disruptor contamination in water.

What Is It?

This project uses yeast as the core of a biosensor. Yeast is a single-celled fungus that is easy to grow and easy to model. In a biosensor, a biological part detects a target, then a reporter part gives a signal you can measure. Here, the idea is to build a Gal1-based system, which means a response tied to the GAL1 promoter, a DNA switch that can turn gene expression on or off.

Think of the system like a smoke alarm. The target chemical is the smoke, the yeast pathway is the sensor, and the color change is the alarm sound. Your version stays safe and educational because you simulate the reaction network in COPASI, a free software tool for modeling biochemical kinetics, instead of trying to build a real genetic circuit in a basic home lab. You can still study how the design responds to changing input levels, noise, and timing.

Why This Is a Good Topic

This is a strong science fair topic because it combines a clear biological problem, a measurable output, and a design you can test in simulation. Estrogen-disruptor contamination matters in water quality, and biosensors are one possible screening tool. You can study how sensitive the circuit would be, how fast it responds, and which design choices make the signal easier to detect. That gives you real engineering choices, not just background reading.

Research Questions

• How does promoter strength affect the simulated color signal from a yeast-based estrogen-disruptor biosensor?
• What is the effect of changing receptor binding affinity on the sensor's detection threshold?
• Does adding a feedback loop improve the model's signal-to-noise ratio for low contaminant levels?
• To what extent do different degradation rates change how fast the biosensor reaches a readable output?
• Which parameter changes most improve separation between contaminated and clean water in the simulation?
• How does the simulated response differ when the model includes matrix effects from tap water compared with pure water?

Basic Materials

• Laptop or desktop computer with internet access
• COPASI software installed
• Spreadsheet software such as Google Sheets or Excel
• Research notebook for model assumptions and parameter tracking
• Yeast, sugar, and basic fermentation supplies for classroom analog demonstrations
• Clear cups or tubes for non-instrumented observation of fermentation analogs
• Kitchen scale with gram accuracy for demo materials
• pH strips for simple fermentation checks
• Timer or stopwatch
• Camera or phone camera for recording color or turbidity changes in analog tests.

Advanced Materials

• Laptop or desktop computer with internet access
• COPASI software installed
• Python with pandas, matplotlib, and scipy for analysis
• ImageJ for image-based color quantification if you run safe visual analog tests
• Access to PubMed for literature review
• Access to NIH, USDA, or EPA water quality resources for background data
• Spectrophotometer if your mentor and lab approve wet validation work
• Micropipettes and sterile tips if you move into supervised lab validation
• Incubator if you do supervised yeast work in a formal lab
• Standard lab notebook and data storage system.

Software & Tools

• COPASI: Simulates biochemical reaction networks and lets you test how kinetic parameters change the output.
• Python: Helps you fit curves, compare simulations, and plot model outputs clearly.
• ImageJ: Measures color intensity in photos if you create a safe visual analog of the reporter signal.
• Google Sheets: Organizes parameters, replicates, and summary statistics in a simple format.
• PubMed: Helps you find review articles and primary papers on yeast biosensors and estrogen disruptors.

Experiment Steps

1. Define the target chemical class and decide which estrogen-disruptor family your model will represent.
2. Map the biological parts you need, including the input sensor, the signal relay, and the color reporter.
3. Build a first-pass reaction scheme in COPASI and choose which parameters you will vary first.
4. Set controls that separate true target response from baseline yeast behavior and tap-water effects.
5. Plan a calibration strategy that turns model output into an interpretable detection score.
6. Compare alternative circuit designs and pick the one with the best balance of sensitivity, speed, and specificity.

Common Pitfalls

• Treating all estrogen-disruptor chemicals as one identical input, which hides real differences in binding and response.
• Using a model with no negative control, which makes it impossible to tell signal from background drift.
• Ignoring tap-water matrix effects, which can change the simulated response compared with clean water.
• Choosing too many free parameters at once, which makes the COPASI fit unstable and hard to interpret.
• Relying on color change alone without a numeric readout, which makes your results too subjective to compare.

What Makes This Competitive

A competitive version of this project would do more than show a working model. You would compare multiple circuit architectures, test sensitivity across realistic contamination ranges, and report uncertainty in a clean way. Strong projects also explain why one design wins over another, not just which one gives the biggest signal. If you can connect your model to a real monitoring need, your work will feel much closer to research than a classroom demo.

Project Variations

• Model the same yeast sensor for a different endocrine-disrupting compound such as bisphenol A or a phthalate analog.
• Compare a colorimetric reporter with a fluorescence reporter in simulation to see which gives cleaner detection windows.
• Add tap-water hardness or pH variation to test how real water chemistry changes the predicted biosensor response.

Learn More

• COPASI Official Site: Free software for simulating biochemical reaction networks, found by searching for the COPASI documentation and download page.
• PubMed: Search review articles on yeast biosensors, endocrine disruptors, and synthetic biology sensing circuits.
• NIH PubMed Central: Search full-text articles on synthetic gene circuits and reporter design.
• USGS Water Science School: Background on water quality, contaminants, and how they move through water systems.
• NOAA National Water Quality Portal: Public data context for environmental contamination and water monitoring.
• MIT OpenCourseWare: Search for systems biology, synthetic biology, or biochemical engineering lecture materials.