A stochastic single-cell based framework for MIC determination

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ID: 314231
2026
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Abstract
AIMS: Antimicrobial resistance, viewed through the One Health approach, represents a global public health challenge connecting humans, animals, and the environment. This study aimed to develop a probabilistic framework linking single-cell variability with population-level Minimum Inhibitory Concentration (MIC) determination, providing a realistic, quantitative understanding of antimicrobial susceptibility and improving interpretation of bacterial responses to antibiotic exposure. METHODS AND RESULTS: The behaviour of individual Escherichia coli cells exposed to gentamicin was examined by time-lapse phase-contrast microscopy, while population growth was quantified using a turbidimetric system simulating the broth microdilution method. Microscopic observations revealed variability in single-cell division and micro-colony formation under antibiotic stress. The maximum micro-colony size (Nmax) decreased with gentamicin concentration, indicating concentration-dependent growth limitation, although limited divisions persisted at 3 μg mL-1. Population-level analysis showed that the first detectable increase in optical density in broth microdilution assays corresponded to approximately 7 log CFU mL-1, defining the operational detection threshold of the conventional MIC assay. Monte Carlo simulations incorporating experimentally estimated single-cell growth probabilities described how detectable growth depends on antibiotic concentration and inoculum size, reframing MIC as a probabilistic value. This framework explained the inoculum effect and the range of antibiotic concentrations near inhibitory concentrations where detection becomes probabilistic due to stochastic responses. CONCLUSIONS: These results demonstrate that variability among individual cells influences the apparent inhibitory effect observed at the population level. Viewing MIC in a probabilistic manner provides a realistic understanding of bacterial behaviour under antibiotic stress and can support reliable interpretations of antimicrobial susceptibility, with potential implications for safety and clinical microbiology.
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Authors Styliani Dimitra Papagianeli, Zafeiro Aspridou, Konstantinos Koutsoumanis
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Journal Journal of applied microbiology
Year 2026
DOI
10.1093/jambio/lxag120
URL
https://doi.org/10.1093/jambio/lxag120
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