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Plants rarely occur in isolated systems. Bacteria can inhabit either theendosphere, the region inside the plant root, or the rhizosphere, the soilregion just outside the plant root. Our goal is to understand if using genomicdata and media dependent metabolic model information is better for trainingmachine learning of predicting bacterial ecological niche than mediaindependent models or pure genome based species trees. We considered threemachine learning techniques: support vector machine, non-negative matrixfactorization, and artificial neural networks. In all three machine-learningapproaches, the media-based metabolic models and flux balance analyses weremore effective at predicting bacterial niche than the genome or PRMT models.Support Vector Machine trained on a minimal media base with Mannose, Prolineand Valine was most predictive of all models and media types with an f-score of0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude thatmedia-based metabolic modeling provides a holistic view of the metabolome,allowing machine learning algorithms to highlight the differences between andcategorize endosphere and rhizosphere bacteria. There was no single media typethat best highlighted differences between endosphere and rhizosphere bacteriametabolism and therefore no single enzyme, reaction, or compound that definedwhether a bacteria's origin was of the endosphere or rhizosphere.

Predicting the Plant Root-Associated Ecological Niche of 21 Pseudomonas Species Using Machine Learning and Metabolic Modeling