Inferring fungal growth rates from optical density data

Area of Science:

• Microbiology
• Mathematical Biology
• Mycology

Background:

• Accurate quantification of fungal growth is essential for effective treatment of severe fungal infections.
• Current methods rely on time-course, morphology-specific data (e.g., hyphal length), which are difficult to collect at scale in clinical settings.

Purpose of the Study:

• To develop a mathematical model for estimating morphology-specific fungal growth rates from indirect optical density (OD600) measurements.
• To overcome limitations of current methods by utilizing easily obtainable data.

Main Methods:

• Proposed a novel mathematical model to infer fungal growth rates from OD600 data.
• The model explicitly incorporates the relationship between OD600 and actual fungal growth, eliminating the need for de novo calibration.
• Applied the model to Aspergillus fumigatus growth.

Main Results:

• The proposed model demonstrated superior performance in fitting and predicting OD600 growth curves compared to reference models.
• The model successfully addressed discrepancies between rates inferred from OD600 and directly measured rates observed in models lacking calibration.
• Achieved accurate estimation of morphology-specific growth rates from indirect measurements.

Conclusions:

• The developed mathematical model provides a robust and accurate method for quantifying fungal growth using accessible OD600 data.
• This approach enhances the feasibility of large-scale fungal growth monitoring in clinical microbiology.
• Offers a valuable tool for improving the management of fungal infections.