Modeling Atrazine Breakdown in Pseudomonas

ISEF Category: Environmental Engineering

Subcategory: Bioremediation  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: 1 to 2 Months

The Hook

Atrazine can move from farm fields into streams and groundwater. That means one herbicide can turn into a watershed problem. You do not need a wet lab to study the first version of this question. You can test the metabolic logic in a computer model first.

What Is It?

Flux-balance simulation is a way to predict how a cell routes materials through its metabolism. Think of metabolism like a subway map. Each station is a chemical reaction, and each line carries atoms from one place to another. COBRApy is a Python tool that helps you build that map and ask what the cell can do under different conditions.

In this project, you model engineered Pseudomonas putida, a soil bacterium known for useful metabolism. You ask how the bacterium might process atrazine, a herbicide used in agriculture. The key idea is simple. If you add or tweak a pathway, does the cell still grow, and can it send carbon or nitrogen toward atrazine breakdown at the same time? Your model cannot prove real-world cleanup by itself, but it can tell you which pathway designs look promising before anyone builds them.

This topic sits at the edge of environmental engineering and synthetic biology. You are not just asking whether a bacterium can survive. You are asking whether its metabolism can be redirected in a way that makes sense for polluted water or soil.

Why This Is a Good Topic

This is a strong science fair topic because you can test real design choices with clear outputs, like growth rate, pathway flux, and tradeoffs between survival and degradation. It connects to a real environmental problem, herbicide runoff in agricultural watersheds, and it lets you study bioremediation without needing to culture a modified organism at first. You can learn model building, constraint-based analysis, and how engineers compare competing designs.

Research Questions

• How does adding an atrazine degradation pathway change predicted growth in Pseudomonas putida?
• What is the effect of different carbon sources on predicted flux toward atrazine breakdown?
• Does reducing oxygen availability change the model's ability to route metabolites through the degradation pathway?
• To what extent does pathway location in the metabolic network affect the predicted biomass yield?
• Which gene knockout candidates increase predicted flow through atrazine degradation without collapsing growth?
• How does the predicted tradeoff between biomass production and atrazine removal change across watershed-like nutrient conditions?

Basic Materials

• Computer with Python installed.
• COBRApy package.
• A public genome-scale metabolic model for Pseudomonas putida.
• Spreadsheet software for tracking model runs and results.
• ImageJ or another image tool if you later compare figures and pathway diagrams.
• A notebook for recording assumptions, constraints, and model versions.

Advanced Materials

• Access to a workstation that can run Python and optimization solvers smoothly.
• COBRApy and a compatible linear programming solver.
• Genome-scale metabolic models for Pseudomonas putida and any edited variants.
• Atrazine pathway reaction data from peer-reviewed papers or curated databases.
• PubChem and KEGG records for checking metabolite identities.
• Optional access to RNA-seq or proteomics data for constraining the model.

Software & Tools

• Python: Runs your analysis scripts and handles model edits, simulations, and plots.
• COBRApy: Builds and solves flux balance models for metabolic networks.
• Jupyter Notebook: Keeps code, notes, and figures in one place for easy revision.
• Cytoscape: Helps you view the metabolic network and spot pathway bottlenecks.
• Excel or Google Sheets: Organizes simulation outputs and compares conditions.

Experiment Steps

1. Define the exact metabolic question you want the model to answer, such as growth versus atrazine removal under different nutrient settings.
2. Choose a starting genome-scale model and check that its organism, assumptions, and reaction set match your project goals.
3. Add the atrazine degradation reactions from the literature and decide where they connect to the host metabolism.
4. Set up a small group of comparison scenarios, including the wild type model, the engineered model, and one or more knockout or constraint cases.
5. Build a plan for measuring success with flux, growth, and tradeoff metrics, then decide how you will compare scenarios fairly.
6. Check your model with sensitivity tests so you know which assumptions drive the results most strongly.

Common Pitfalls

• Using a pathway from one paper without checking whether the reactions are balanced, which can make the simulation impossible or misleading.
• Forgetting to match metabolite names and IDs between the atrazine pathway and the host model, which breaks model integration.
• Treating the model's best growth solution as proof that the engineered strain will work in a real watershed, which overstates what flux balance analysis can say.
• Comparing scenarios with different objective functions or constraint settings, which makes the results unfair.
• Ignoring whether the degradation pathway creates a bottleneck in cofactors like ATP, NADH, or oxygen, which hides the real design limit.

What Makes This Competitive

A competitive version of this project would do more than run one model once. You would compare multiple pathway designs, test several environmental conditions, and explain why one design wins with flux analysis instead of just graphing a single output. Strong projects also check mass balance, compare different objective functions, and use sensitivity analysis to show which assumptions matter most. That kind of careful modeling looks much stronger than a basic simulation.

Project Variations

• Model atrazine degradation in a different host, such as a soil microbe with a simpler metabolic network.
• Compare aerobic and low-oxygen constraints to see how oxygen changes predicted cleanup capacity.
• Test how adding or removing nitrogen or carbon sources changes the tradeoff between biomass and herbicide breakdown.

Learn More

• COBRApy documentation: The official package docs explain model setup, flux balance analysis, and common workflows, and you can find them by searching for COBRApy docs.
• NCBI PubMed: Search for review articles on atrazine biodegradation, Pseudomonas putida metabolism, and constraint-based metabolic modeling.
• KEGG: Use the pathway database to check metabolite names, enzyme links, and reaction context for atrazine-related metabolism.
• BiGG Models: Browse curated genome-scale metabolic models and reaction IDs that can help you compare host metabolism and edit a model cleanly.
• MIT OpenCourseWare: Search for systems biology or metabolic engineering lectures that explain network thinking and optimization basics.