Quantifying phage infectivity from characteristics of bacterial population dynamics

Area of Science:

• Microbiology
• Bacteriology
• Virology

Background:

• Phage biology research frequently aims to quantify phage efficacy in controlling bacterial populations.
• Traditional methods for this quantification are often time-consuming, hindering experimental throughput.

Purpose of the Study:

• To develop and validate a high-throughput method for quantifying phage efficacy using bacterial population dynamics.
• To identify key metrics from bacterial growth curves that accurately predict short-term infectivity and long-term phage suppression.

Main Methods:

• Utilized mathematical modeling to simulate bacterial population dynamics with known phage and bacterial traits.
• Employed high-throughput microplate readers (automated spectrophotometers) to measure bacterial population dynamics.
• Evaluated various dynamic metrics (e.g., growth rate, peak density, extinction time) for their predictive power.

Main Results:

• Multiple metrics effectively predict short-term phage infectivity and are highly correlated.
• Bacterial dynamics metrics can accurately predict phage growth rate, quantifying combined phage trait effects.
• Peak density, time of peak density, and extinction time are optimal metrics for cross-host comparisons and long-term studies.

Conclusions:

• Established a theoretical and practical framework for quantifying phage-host interactions using bacterial population dynamics in microplate readers.
• This approach supports the design of more efficient in vitro experiments in phage biology and synthetic biology.
• The findings facilitate the quantitative assessment of phage efficacy, crucial for applications like phage therapy.