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BacterAI maps microbial metabolism without prior knowledge


Training artificial intelligence (AI) systems to perform autonomous experiments would vastly increase the throughput of microbiology; however, few microbes have large enough datasets for training such a system. In the present study, we introduce BacterAI, an automated science platform that maps microbial metabolism but requires no prior knowledge. BacterAI learns by converting scientific questions into simple games that it plays with laboratory robots. The agent then distils its findings into logical rules that can be interpreted by human scientists. We use BacterAI to learn the amino acid requirements for two oral streptococci: Streptococcus gordonii and Streptococcus sanguinis. We then show how transfer learning can accelerate BacterAI when investigating new environments or larger media with up to 39 ingredients. Scientific gameplay and BacterAI enable the unbiased, autonomous study of organisms for which no training data exist.

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Fig. 1: BacterAI uses an automated experiment pipeline and an AI agent to study bacterial metabolism.
Fig. 2: BacterAI selects experiments to train an internal neural network that predicts the fitness of S. gordonii.
Fig. 3: Transfer learning accelerates BacterAI.
Fig. 4: BacterAI quickly learns to predict growth for S. sanguinis in CDM with 39 ingredients.

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This research was supported by the National Institutes of Health (grant nos. EB027396 and GM138210 to P.J.). The Titan V used for this research was donated by the NVIDIA Corporation. We thank K. Janes for his comments on the manuscript. Figures 1 and 3 were created with

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Authors and Affiliations



A.D. and P.J. conceived the study, implemented BacterAI, designed experiments, analysed data and wrote the manuscript. A.D., K.K., D.L., A.L., N.S. and K.J. optimized laboratory workflows, executed experiments and performed quality control on the data.

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Correspondence to Paul A. Jensen.

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The authors declare no competing interests.

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Nature Microbiology thanks Nathan Price and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 BacterAI learns the amino acid requirements of S. gordonii.

BacterAI learns to predict the growth of S. gordonii in media containing combinations of amino acids. Over 13 days, the agent trains a neural network to guide the search for new experiments along the growth front. Each day, the neural network is retrained using the data from all previous data (train set). The accuracy of the model is measured using the 336 experiments selected each day (test set).

Extended Data Fig. 2 Media selected by BacterAI are not random.

The fitness of S. gordonii grown in randomly selected media clusters near no growth (0) and full growth (1). By contrast, experiments selected by BacterAI are more uniformly distributed.

Extended Data Fig. 3 BacterAI learns the amino acid requirements of S. sanguinis.

BacterAI selects experiments to learn the amino acid requirements of the bacterium Streptococcus sanguinis. Although S. sanguinis is genetically similar to S. gordonii, the bacteria have different amino acid auxotrophies. BacterAI began its investigation of S. sanguinis from a blank slate and did not carry over any knowledge from the previous experiments with S. gordonii.

Extended Data Fig. 4 BacterAI learns amino acid requirements of S. sanguinis using transfer learning.

BacterAI learns a growth model for S. gordonii using transfer learning. A growth model for S. sanguinis was used to select the initial experiments and was retrained with new growth data from S. gordonii.

Extended Data Fig. 5 BacterAI learns amino acid requirements of S. sanguinis in anaerobic conditions using transfer learning.

BacterAI learns an anaerobic growth model for S. sanguinis using transfer learning. A growth model for S. sanguinis in an aerobic (5% CO2) environment was used to select the initial experiments and was retrained with new growth data from anaerobic experiments.

Extended Data Fig. 6 Replicate growth assays show little variation.

Replicates from fourteen days of experiments with S. sanguinis show little variation in growth. Using a grow/no grow threshold of 0.25 gives 97.37% (1 vs. 2), 97.11% (1 vs. 3), and 97.39% (2 vs. 3) agreement between the replicates.

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Dama, A.C., Kim, K.S., Leyva, D.M. et al. BacterAI maps microbial metabolism without prior knowledge. Nat Microbiol 8, 1018–1025 (2023).

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