A machine learning framework for the prediction and analysis of bacterial antagonism in biofilms using morphological descriptors

Biofilms are structured microbial communities that promote cell interactions through close spatial organization, leading to cooperative or competitive behaviors. Predicting microbial interactions in biofilms could aid in developing innovative strategies to prevent the colonisation of undesirable bacteria. Here, we present a machine learning approach to predict the antagonistic effects of beneficial bacterial candidates Bacillus and Paenibacillus species against undesirable bacteria ( Staphylococcus aureus , Enterococcus cecorum , Escherichia coli and Salmonella enterica ), based on the morphological descriptors of single-species biofilms. We trained the models using quantitative features (e.g. biofilm volume, thickness, roughness, or substratum coverage). As a proxy for antagonism, an exclusion score was used as the supervised training target. The latter was calculated based on the ratio of biofilm volume between the undesirable bacteria and the beneficial strain. We subsequently applied diverse explainability methods to analyze the resulting model and found insights highlighting the importance of biofilm formation context when predicting antagonism. Our results demonstrate that machine learning offers an efficient, data-driven tool to predict microbial interactions within biofilms and support the selection of competitive beneficial strains against pathogens. This approach enables scalable screening of microbial interactions, making it applicable to both research and biotechnological applications. • Machine learning approach to predict bacterial antagonism within biofilms. • Custom evaluation protocol named Homology-Controlled Cross-Validation. • Proof of concept to help optimize strain screening in the design of beneficial biofilms. • Initial benchmark of linear and non-linear methods on this novel task.