Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

ABSTRACT Microbes affect each other’s growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Demetrius DiMucci, Mark Kon, Daniel Segrè
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2018
Materias:	coculture experiments ecological networks flux balance analysis machine learning metabolic modeling microbial interactions Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/eea9e7e7c3ba4e66ba4e2fcd9c5a3aba
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:eea9e7e7c3ba4e66ba4e2fcd9c5a3aba
record_format	dspace
spelling	oai:doaj.org-article:eea9e7e7c3ba4e66ba4e2fcd9c5a3aba2021-12-02T19:47:34ZMachine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks10.1128/mSystems.00181-182379-5077https://doaj.org/article/eea9e7e7c3ba4e66ba4e2fcd9c5a3aba2018-10-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00181-18https://doaj.org/toc/2379-5077ABSTRACT Microbes affect each other’s growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other’s growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.Demetrius DiMucciMark KonDaniel SegrèAmerican Society for Microbiologyarticlecoculture experimentsecological networksflux balance analysismachine learningmetabolic modelingmicrobial interactionsMicrobiologyQR1-502ENmSystems, Vol 3, Iss 5 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	coculture experiments ecological networks flux balance analysis machine learning metabolic modeling microbial interactions Microbiology QR1-502
spellingShingle	coculture experiments ecological networks flux balance analysis machine learning metabolic modeling microbial interactions Microbiology QR1-502 Demetrius DiMucci Mark Kon Daniel Segrè Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks
description	ABSTRACT Microbes affect each other’s growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other’s growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.
format	article
author	Demetrius DiMucci Mark Kon Daniel Segrè
author_facet	Demetrius DiMucci Mark Kon Daniel Segrè
author_sort	Demetrius DiMucci
title	Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks
title_short	Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks
title_full	Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks
title_fullStr	Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks
title_full_unstemmed	Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks
title_sort	machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks
publisher	American Society for Microbiology
publishDate	2018
url	https://doaj.org/article/eea9e7e7c3ba4e66ba4e2fcd9c5a3aba
work_keys_str_mv	AT demetriusdimucci machinelearningrevealsmissingedgesandputativeinteractionmechanismsinmicrobialecosystemnetworks AT markkon machinelearningrevealsmissingedgesandputativeinteractionmechanismsinmicrobialecosystemnetworks AT danielsegre machinelearningrevealsmissingedgesandputativeinteractionmechanismsinmicrobialecosystemnetworks
_version_	1718375999725371392

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

Ejemplares similares