ICMCS Seminar - Unravelling Phage-Bacteria Interactions: Quantifying and Predicting through Growth Kinetics and Machine Learning

Given the problems with antimicrobial resistance, bacteriophages are being investigated as promising candidates for treating multidrug resistant bacteria. Because phages’ range of infection is usually at the strain level, selecting the appropriate phage for a given bacterial infection is challenging. In this project, we implement machine learning (ML) analyses to understand the biology underlying phage-bacteria interactions and to make better decisions when selecting phages for therapy. 

We worked with a library of 301 Uropathogenic Escherichia coli strains challenged against 31 phages. The bacterial growth kinetics were measured in liquid assays. Additionally, the bacteria and phages were whole genome sequenced, and pangenome analyses were performed [1]. The performance of phages infecting bacteria was measured in terms of the area under the curve. We trained ML models to predict the value of this measurement for unseen bacterium-phage pairs, based on their genomes. In our pipeline, a recursive feature elimination process is done, from which we can obtain the most statistically relevant genes, both in bacteria and phages. This can give valuable insights into the biological processes involved, which is crucial for engineering new phages.

[1] doi.org/10.1073/pnas.2313574121

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Meeting ID: 812 1147 0751
Passcode: ICMCS123