Inferring phage-bacteria infection networks from time-series data

Area of Science:

• Microbiology
• Ecology
• Bioinformatics

Background:

• Phage-bacteria interactions form complex infection networks crucial for microbial community dynamics.
• Traditional plaque assays are limited in scalability and struggle with unculturable microbes in environmental samples.

Purpose of the Study:

• To develop and validate an alternative method for inferring phage-bacteria infection networks.
• To overcome the limitations of current culture-based methods for diverse and unculturable microbial communities.

Main Methods:

• Utilizing time-series data of fluctuating population densities to estimate interaction networks.
• Employing in silico experiments to analyze network reconstruction quality and identify robust regimes.
• Implementing a multi-experiment approach to combine time-series data and improve network estimation.

Main Results:

• The proposed method successfully infers complete phage-bacteria interaction networks from population dynamics.
• In silico analyses identified conditions enabling accurate network reconstruction.
• Combining data from multiple experiments enhanced network estimation and mitigated evolutionary effects.

Conclusions:

• Time-series population data offers a scalable alternative to plaque assays for mapping phage-bacteria interactions.
• The developed method provides a robust framework for understanding complex microbial community structures.
• This approach facilitates the study of microbial ecology in diverse and challenging environments.