Inferring fungal growth rates from optical density data

by Tara Hameed, Natasha Motsi, Elaine Bignell, Reiko J. Tanaka

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD₆₀₀) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD₆₀₀ being an indirect measure by explicitly including the relationship between the indirect OD₆₀₀ measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD₆₀₀ growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD₆₀₀ versus directly measured data in reference models that did not include calibration.