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Updated: Jul 24, 2025

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
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理论和实践中的微生物相互作用:何时测量与模型相容?

Aurore Picot1, Shota Shibasaki2, Oliver J Meacock3

  • 1Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, Paris, France; Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.

Current opinion in microbiology
|July 8, 2023
PubMed
概括

一般化的Lotka-Volterra (gLV) 模型不适合研究批量培养中的微生物相互作用. 为更准确的微生物生态系统建模,建议采用替代实验和理论方法.

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Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

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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Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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