Feature-Based Growth Curve Classification Enables Efficient Phage Discrimination

Rapid isolation of therapeutic bacteriophages from environmental sources is essential for personalized phage therapy, particularly when appropriate phages are unavailable in existing banks. However, comprehensive characterization of all candidate phages is resource-intensive, especially when plaque morphologies are similar and fail to discriminate between distinct phages. Here, we present an upstream screening approach that utilizes co-culture growth curve analysis to rapidly triage phage isolates during the early isolation process. We extracted seven biologically meaningful features that capture lysis kinetics, lysis efficiency, and post-lysis dynamics from bacterial growth curves and applied unsupervised clustering algorithms for phage discrimination. Validation using T-phages at a multiplicity of infection of 0.01 demonstrated superior clustering performance (Adjusted Rand Index = 0.881 ± 0.057) compared to established metrics including the Virulence Index and Centroid Index. Application to phages isolated from sewage successfully identified all three genomically distinct species present (sampling score = 1.0), enabling targeted selection of representative phages for downstream characterization. This approach reduced candidates requiring detailed analysis by two-thirds (from 21 to 7 isolates) while maintaining complete species coverage, thereby providing an efficient and scalable screening tool that reduces workload for downstream analyses and accelerates discovery of novel therapeutic phages for clinical applications.