ArticleMicrobiome2019
An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data.
Article in Microbiome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Correction: Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations.PLoS computational biology · 2026Article
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- Toward the Next Generation of In Silico Modeling of Dynamic Host-Microbiota Interactions in the Skin.JID innovations : skin science from molecules to population health · 2025Review
- LorDist: a novel method for calculating the distance based on functional data analysis with application to longitudinal microbial data.Microbiology spectrum · 2025Article
- A computational framework for inferring species dynamics and interactions with applications in microbiota ecology.NPJ systems biology and applications · 2025Article
- Predicting gut microbiota dynamics in obese individuals from cross-sectional data.Frontiers in cellular and infection microbiology · 2025Article
- Bridging Ecology and Microbiomes: Applying Ecological Theories in Host-associated Microbial Ecosystems.Current clinical microbiology reports · 2025Review
- Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation.mSystems · 2024Article
- A single intranasal dose of essential oil spray confers modulation of the nasopharyngeal microbiota and short-term inhibition of Mannheimia in feedlot cattle: a pilot study.Scientific reports · 2024Article
- Ecological modelling approaches for predicting emergent properties in microbial communities.Nature ecology & evolution · 2022Review
- Quantifying the impact of ecological memory on the dynamics of interacting communities.PLoS computational biology · 2022Article
- BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data.PLoS computational biology · 2021Article
- Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model.Bioinformatics (Oxford, England) · 2021Article
- Quantifying Live Microbial Load in Human Saliva Samples over Time Reveals Stable Composition and Dynamic Load.mSystems · 2021Article
- Network analysis methods for studying microbial communities: A mini review.Computational and structural biotechnology journal · 2021Review
- Next Generation Microbiome Research: Identification of Keystone Species in the Metabolic Regulation of Host-Gut Microbiota Interplay.Frontiers in cell and developmental biology · 2021Review
- Modeling of Urinary Microbiota Associated With Cystitis.Frontiers in cellular and infection microbiology · 2021Article
- Compositional Lotka-Volterra describes microbial dynamics in the simplex.PLoS computational biology · 2020Article
- Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge.BMC ecology · 2020Article
- From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes.Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to directly learn the ecological models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is severely limited by the lack of accurate absolute cell density measurements (biomass).
methodsWe present a new computational approach that resolves this key limitation in the inference of generalized Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference with an expectation-maximization algorithm (BEEM).
resultsBEEM outperforms the state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM's application to previously inaccessible public datasets (due to the lack of biomass data) allowed us to construct ecological models of microbial communities in the human gut on a per-individual basis, revealing personalized dynamics and keystone species.
conclusionsBEEM addresses a key bottleneck in "systems analysis" of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements.
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