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Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
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The bacterial growth curve is a fundamental concept in microbiology that describes the dynamics of bacterial population growth in a closed system with controlled environmental conditions, such as temperature and nutrient availability. This curve is divided into four distinct phases: lag, log (exponential), stationary, and death phases, each reflecting a unique stage of bacterial adaptation and growth. During the lag phase, bacteria acclimate to their surroundings by synthesizing essential...
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High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
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Bacterial colony size growth estimation by deep learning.

Sára Ágnes Nagy1, László Makrai2, István Csabai3

  • 1Centre for Bioinformatics, University of Veterinary Medicine, 1078, Budapest, Hungary.

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|October 25, 2023
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Summary

Convolutional neural networks (CNNs) accurately detect bacterial colonies and predict growth rates from images. This AI approach offers an efficient tool for bacteriology, aiding pathogenicity and food safety research.

Keywords:
Bacterial growth rateDeep learningNeural network

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Area of Science:

  • Microbiology
  • Computer Science
  • Bioinformatics

Background:

  • Bacterial growth rate is crucial for pathogenicity and food safety.
  • Monitoring bacterial growth provides vital medical and veterinary data.
  • Accurate quantification of bacterial growth dynamics is essential for research.

Purpose of the Study:

  • To develop and validate convolutional neural networks (CNNs) for bacterial colony detection and growth rate estimation.
  • To analyze the impact of colony density and rifampicin pretreatment on bacterial growth dynamics.
  • To assess the efficacy of AI-driven image analysis in bacteriological research.

Main Methods:

  • Training CNNs on manually annotated images of bacterial cultures on solid medium.
  • Estimating bacterial colony size and growth rates using image sequences of Staphylococcus aureus.
  • Employing linear and mixed-effect models to analyze growth data and influencing factors.

Main Results:

  • CNNs accurately detected bacterial colonies and predicted growth rates.
  • Mean growth rate in control cultures was estimated at 60.3 units/h within the first 24 hours.
  • Colony growth rate was reduced by increased neighboring colonies, particularly in control groups, and by rifampicin pretreatment (36.5 units/h).

Conclusions:

  • CNN-based bacterial colony detection is an accurate and efficient method.
  • AI analysis of bacterial colony growth dynamics offers a valuable tool for bacteriology.
  • This approach can significantly advance research in pathogenicity and food safety.