  Plants rarely occur in isolated systems. Bacteria can inhabit either the
endosphere, the region inside the plant root, or the rhizosphere, the soil
region just outside the plant root. Our goal is to understand if using genomic
data and media dependent metabolic model information is better for training
machine learning of predicting bacterial ecological niche than media
independent models or pure genome based species trees. We considered three
machine learning techniques: support vector machine, non-negative matrix
factorization, and artificial neural networks. In all three machine-learning
approaches, the media-based metabolic models and flux balance analyses were
more effective at predicting bacterial niche than the genome or PRMT models.
Support Vector Machine trained on a minimal media base with Mannose, Proline
and Valine was most predictive of all models and media types with an f-score of
0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude that
media-based metabolic modeling provides a holistic view of the metabolome,
allowing machine learning algorithms to highlight the differences between and
categorize endosphere and rhizosphere bacteria. There was no single media type
that best highlighted differences between endosphere and rhizosphere bacteria
metabolism and therefore no single enzyme, reaction, or compound that defined
whether a bacteria's origin was of the endosphere or rhizosphere.
