HaloUMI: Physics-informed analysis of inhibition halo assays
Quantification of microbial growth inhibition is central to assays from antibiotic susceptibility to antifungal sensitivity, yet existing approaches struggle with irregular inhibition zones and variation in microbial lawn density. Here, we present Halo Unbiased Measurement of growth Inhibition (HaloUMI), an open-source Python graphical interface for automated, high-throughput analysis of lawn-based microbial assays. HaloUMI integrates robust image processing with physics-informed modelling to quantify inhibition zones without assuming circular geometry, enabling analysis of uniform and irregular halo phenotypes. Using diffusion- and growth-based physical modelling, HaloUMI experimentally validates a correction for variation in microbial lawn density, a major source of assay variability that can confound quantitative comparison of inhibition phenotypes. Validation using simulations and yeast killer-toxin assays demonstrates precise, reproducible measurement across diverse conditions. HaloUMI is applicable to multiple assay formats, including microbial interaction, mating, and conventional disc-diffusion assays, providing an accessible and generalisable framework for quantitative analysis of microbial growth inhibition.