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Inferring fungal growth rates from optical density data

Summary

Scientists have developed a new mathematical method that allows doctors and lab technicians to measure fungal growth rates more accurately using simple optical density measurements. This approach doesn’t require expensive equipment or specialized knowledge, making it accessible to regular medical labs. The method could help doctors better assess how well antifungal drugs are working and detect resistant infections earlier.

Background

Quantifying fungal growth is essential for antifungal drug discovery and monitoring resistance, but current methods require time-consuming collection of morphology-specific data. Optical density (OD) measurements provide quick, indirect measures of fungal growth widely used in laboratories, but changes in OD may not accurately reflect true fungal growth due to detection limits and morphological changes.

Objective

This study proposes a mathematical model to estimate morphology-specific fungal growth rates from indirect OD600 measurements without requiring separate calibration data collection. The model explicitly incorporates the relationship between OD600 measurements and true fungal growth to improve accuracy of growth rate estimation.

Results

The proposed Logistic-OD-calibration model successfully fit OD600 data across all inoculum sizes and outperformed reference models in predictive accuracy. Hyphal growth rates inferred from OD600 using the calibration-inclusive model aligned with growth rates estimated from direct morphological measurements, whereas models without calibration produced significantly lower rates.

Conclusion

The model provides a practical approach for clinical microbiology laboratories to estimate morphology-specific fungal growth rates from routine OD600 measurements without collecting calibration data, enabling quantitative temporal and morphology-specific evaluation of fungal growth for antifungal drug testing.
  • Published in:PLoS Computational Biology,
  • Study Type:Mathematical Modeling Study,
  • Source: PMID: 38753887, DOI: 10.1371/journal.pcbi.1012105
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