Growth Curve
Chart overview
Growth curves are fundamental to microbiology, cell biology, and bioprocess engineering.
Key points
- An OD600 absorbance measurement taken at regular intervals traces three canonical phases: the lag phase where cells adapt to the medium, the exponential (log) phase where cells divide at maximum rate, and the stationary phase where nutrient depletion or waste accumulation limits growth.
- A fourth death phase is sometimes included.
- Fitting the Gompertz or logistic model to the data allows extraction of key parameters: maximum specific growth rate (mu_max), lag time (lambda), and carrying capacity (K).
Python Tutorial
How to create a growth curve in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Complete Guide to Scientific Data VisualizationExample Visualization
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"Create a bacterial growth curve from my OD600 time-series data. Plot mean OD600 with standard deviation error bars for each condition. Fit a logistic growth model to each curve using scipy and overlay the fitted lines. Annotate the calculated doubling time and maximum growth rate on the plot. Use a log scale on the y-axis. Format for publication in a microbiology journal at 300 DPI."
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Python Code Example
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Common Use Cases
- 1Characterizing bacterial growth kinetics in response to nutrients, temperature, or pH changes
- 2Comparing antibiotic sensitivity by monitoring growth inhibition curves (MIC assays)
- 3Cell proliferation assays in cancer biology using WST-1 or CellTiter-Glo reagents
- 4Bioprocess optimization: monitoring biomass in fed-batch fermentation runs
Pro Tips
Plot on a semi-log y-axis to linearize the exponential phase and make doubling time visually apparent
Include at least three biological replicates and show individual data points alongside mean lines
Normalize all curves to the same starting OD to enable fair cross-condition comparisons
Report model fit quality (R-squared or residual sum of squares) alongside extracted parameters
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for growth-curve.
numpy
Useful in specialized workflows that complement core Python plotting libraries for growth-curve analysis tasks.
scipy
Useful in specialized workflows that complement core Python plotting libraries for growth-curve analysis tasks.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.