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Updated: May 8, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary

Adriana Lucia-Sanz¹, Shengyun Peng², Chung Yin Joey Leung³

¹School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Virus Evolution

|December 25, 2024

Summary

Predicting phage-bacteria interactions is challenging. Machine learning accurately forecasts infection outcomes by analyzing phage and bacterial genetics, identifying key mutations driving these interactions.

Keywords:

coevolution driver genotype machine learning mutation phage phenotype

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Following Cell-fate in E. coli After Infection by Phage Lambda

Following Cell-fate in E. coli After Infection by Phage Lambda

Published on: October 14, 2011

Area of Science:

Microbiology
Genetics
Bioinformatics

Background:

Bacteriophage (phage) and bacteria interactions are complex due to uncharacterized genetic factors.
Predicting specific phage-host interactions remains a significant challenge in microbial ecology.

Purpose of the Study:

To develop a machine learning framework for predicting phage-bacteria interactions.
To identify genetic drivers influencing phage infection outcomes.

Main Methods:

Trained a machine learning model on genome sequences and phenotypic interactions of coevolved Escherichia coli and phage lambda strains.
Employed multiple inference strategies without prior knowledge of driver mutations.
Predicted infection phenotypes and quantitative infection levels for 2,295 potential interactions.

Main Results:

The most effective model accurately predicted 86% of phage-bacteria interactions.
Reduced the error in estimating infection strength by 40%.
Identified key phage lambda and E. coli mutations influencing interaction outcomes, including novel resistance-associated mutations.

Conclusions:

Machine learning can accurately predict phage-bacteria infection outcomes and identify genetic determinants.
This approach advances understanding of coevolutionary dynamics and can inform strategies for complex microbial communities.