E. coli Cross-Feeding Networks in Co-Culture

ISEF Category: Microbiology

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

The Hook

Some bacteria can survive only if a neighbor hands over the molecule they cannot make. That turns tiny cells into a social network with rules you can test. You can map who helps whom, then check whether a computer model predicts the same partnerships. This project mixes wet lab work, graph thinking, and real microbial ecology.

What Is It?

An auxotroph is a microbe with a missing biosynthetic skill. Think of it like a chef who lost the recipe for one ingredient. On its own, that cell cannot grow unless the missing metabolite comes from the environment.

Cross-feeding happens when one strain leaks, shares, or releases a compound that another strain needs. In a pairwise co-culture, two strains may each lack something different, but together they can survive because each one supplies the other. When you test many pairings, you can draw a network that shows which strains form obligate partnerships and which ones do not.

FBA, or flux balance analysis, is a computer method that predicts how metabolites move through a cell’s metabolic network. Cobrapy lets you run FBA on a genome-scale model such as iJO1366. For this project, you compare real growth patterns from co-cultures with model predictions about which pairings should support exchange.

Why This Is a Good Topic

This is a strong science fair topic because you can turn a hidden metabolic process into clear yes-or-no growth data, then push it further with network analysis and modeling. It connects to microbiome interactions, synthetic ecology, and how microbes survive in mixed communities. You can learn how to design controls, read growth outcomes, build a graph from experimental data, and test whether a metabolic model matches reality.

Research Questions

• How does pairing different E. coli auxotroph strains change co-culture growth compared with each strain alone?
• What is the effect of strain identity on whether a pair forms an obligate-cooperation link?
• Does the direction of cross-feeding match the metabolite needs predicted from each auxotroph genotype?
• To what extent do FBA predictions from iJO1366 match the observed obligate-cooperation graph?
• Which strain pairs support growth only when both partners are present, but not when either strain is grown alone?
• What is the effect of adding a shared nutrient on the number of obligate-cooperation links you observe?

Basic Materials

• E. coli K-12 educational auxotroph kit with clearly labeled mutant strains.
• Sterile culture tubes, Petri dishes, or microplate materials approved for your lab setup.
• Appropriate growth medium for the kit strains.
• Sterile pipette tips and micropipettes.
• Inoculating loops or sterile spreaders.
• Incubator or temperature-controlled growth space.
• Spectrophotometer or plate reader for growth measurements.
• Parafilm or sealing film for culture containment.
• Permanent marker and lab notebook for sample tracking.
• Digital camera or phone camera for documenting colony or turbidity changes.

Advanced Materials

• E. coli K-12 auxotroph strains with known genotype annotations.
• Defined minimal media and selective supplements for mapping exchange needs.
• Sterile 96-well plates or deep-well plates for multiplexed co-cultures.
• Plate reader with kinetic growth measurement capability.
• Spectrophotometer for optical density measurements.
• High-quality balance and calibrated pipettes for media preparation.
• Incubator shaker if your protocol uses liquid co-culture.
• DNA sequencing access or genotype confirmation data from the kit provider.
• Computer with Python and Cobrapy for FBA.
• Genome-scale metabolic model iJO1366 in a format compatible with Cobrapy.

Software & Tools

• Cobrapy: Runs flux balance analysis on the iJO1366 metabolic model and tests predicted metabolite exchange links.
• Python: Organizes growth data, builds the cooperation graph, and runs simple statistics.
• Jupyter Notebook: Keeps code, notes, and figures together in one place.
• ImageJ: Measures colony or turbidity image intensity if you document growth by photo.
• Graphviz: Draws the obligate-cooperation network in a clean visual format.

Experiment Steps

1. Define the strain panel you will test, and list which metabolite each auxotroph cannot make.
2. Choose one growth readout, then decide how you will call a pair cooperative, noncooperative, or ambiguous.
3. Plan the control set that separates true cross-feeding from shared-media carryover, contamination, and supplement leakage.
4. Build a testing matrix for all pairwise co-cultures, then decide how you will record outcomes in a consistent table.
5. Design the graph rules that convert growth outcomes into edges, direction, and confidence levels.
6. Set up the FBA comparison, then decide which model outputs count as a predicted exchange match or mismatch.

Common Pitfalls

• Mixing up true cross-feeding with leftover nutrients from the inoculum, which can make weak pairs look cooperative.
• Calling any slow growth a partnership, which inflates the obligate-cooperation graph with false edges.
• Using uneven starting cell densities, which makes one strain dominate and hides the exchange effect.
• Comparing experiment results to FBA without matching the same growth conditions in the model, which makes the prediction test meaningless.
• Forgetting to confirm strain genotypes or auxotroph labels, which can turn the whole pairing map into the wrong network.

What Makes This Competitive

A stronger project will not stop at showing that some pairs grow together. You can make it more competitive by testing many pairings, defining a careful rule for cooperation, and quantifying how often the model gets the direction or presence of exchange right. Strong analysis matters here, especially if you compare network structure, not just individual growth outcomes. A sharp discussion of mismatches between biology and FBA can make your work stand out.

Project Variations

• Test whether auxotroph pairs behave differently in liquid culture versus on solid media.
• Compare pairwise cross-feeding networks across two E. coli K-12 auxotroph panels with different missing pathways.
• Add a graph analysis step that measures whether cooperative pairs cluster by shared metabolic pathway or genotype type.

Learn More

• NCBI Bookshelf: Search for free textbook chapters on bacterial metabolism, auxotrophy, and microbial interactions.
• PubMed: Search for review articles on cross-feeding, auxotrophs, and synthetic microbial communities.
• NIH iJO1366 model resources: Search for papers and model files related to the E. coli K-12 genome-scale metabolic model.
• Cobrapy documentation: Read the free user guide and examples for running flux balance analysis in Python.
• Molecular Genetics of Bacteria chapters on metabolic mutants: Look for free university or open textbook materials that explain auxotrophs and selection.
• ASM resources: Search the American Society for Microbiology site for educational articles on bacterial growth and metabolism.