A fundamental challenge in microbial ecology is determining whether bacteria compete 🦠🥊🦠 or cooperate 🦠🤝🦠 in different environmental conditions. With recent advances in genome-scale metabolic models, we are now capable of simulating interactions between thousands of pairs of bacteria in thousands of different environmental settings at a scale infeasible experimentally. 🤯 These approaches can generate tremendous amounts of data that can be exploited by state-of-the-art machine learning algorithms to uncover the mechanisms driving interactions. 🧬

In collaboration with Eric Libby’s group at Umeå University’s Integrated Science Lab (IceLab) 🧊, our team developed Friend or Foe, a compendium of 64 tabular environmental datasets, consisting of more than 26M shared environments for more than 10K pairs of bacteria sampled from two of the largest collections of metabolic models. 🦠 The Friend or Foe datasets are curated for a wide range of machine learning (ML) tasks – supervised, unsupervised, and generative – to address specific questions underlying bacterial interactions. We benchmarked a selection of the most recent ML models for each of these tasks, and our results indicate that ML can be successful in this application to microbial ecology. 🦾 Going beyond, analyses of the Friend or Foe compendium can shed light on the predictability of bacterial interactions and highlight novel research directions into how bacteria infer and navigate their relationships. 💡

Check out the preprint for yourself, out now via arXiv! 🦠