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

Abstract

Plants rarely occur in isolated systems. Bacteria can inhabit either the\nendosphere, the region inside the plant root, or the rhizosphere, the soil\nregion just outside the plant root. Our goal is to understand if using genomic\ndata and media dependent metabolic model information is better for training\nmachine learning of predicting bacterial ecological niche than media\nindependent models or pure genome based species trees. We considered three\nmachine learning techniques: support vector machine, non-negative matrix\nfactorization, and artificial neural networks. In all three machine-learning\napproaches, the media-based metabolic models and flux balance analyses were\nmore effective at predicting bacterial niche than the genome or PRMT models.\nSupport Vector Machine trained on a minimal media base with Mannose, Proline\nand Valine was most predictive of all models and media types with an f-score of\n0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude that\nmedia-based metabolic modeling provides a holistic view of the metabolome,\nallowing machine learning algorithms to highlight the differences between and\ncategorize endosphere and rhizosphere bacteria. There was no single media type\nthat best highlighted differences between endosphere and rhizosphere bacteria\nmetabolism and therefore no single enzyme, reaction, or compound that defined\nwhether a bacteria's origin was of the endosphere or rhizosphere.\n