ArticlePLoS computational biology2017
Two dynamic regimes in the human gut microbiome.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
71 citing papers in PubMed.
- Sequential laxative-probiotic usage for treatment of irritable bowel syndrome: a novel method inspired by mathematical modelling of the microbiome.Scientific reports · 2020Trial
- Gut microbial diversity and candidate keystone taxa in Indian tribes: Insights across lifestyle-ecological continuum and health associations.Current research in microbial sciences · 2026Article
- Bacteriophage Density Influences the Rate of Resistance Evolution.Bulletin of mathematical biology · 2026Article
- Macroecological Laws Can Naturally Arise from Chaotic Internal Species Dynamics.bioRxiv : the preprint server for biology · 2025Article
- Bacteriophage density influences the rate of resistance evolution.bioRxiv : the preprint server for biology · 2025Article
- MIMIC: a Python package for simulating, inferring, and predicting microbial community interactions and dynamics.Bioinformatics (Oxford, England) · 2025Article
- Microbial functional guilds respond cohesively to rapidly fluctuating environments.bioRxiv : the preprint server for biology · 2025Article
- Warming-driven migration of enterotypes mediates host health and disease statuses in ectotherm Litopenaeus vannamei.Communications biology · 2025Article
- Neonatal gut microbiota stratification and identification of SCFA-associated microbial subgroups using unsupervised clustering and machine learning classification.Frontiers in microbiology · 2025Article
- Microbiome time series data reveal predictable patterns of change.Microbiology spectrum · 2024Article
- Article
- A rigorous and versatile statistical test for correlations between stationary time series.PLoS biology · 2024Article
- mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics.PLoS computational biology · 2024Article
- Progress on network modeling and analysis of gut microecology: a review.Applied and environmental microbiology · 2024Review
- Article
- Towards a fundamental theory of taxon transitions in microbial communities.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Structured community transitions explain the switching capacity of microbial systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes.Nature communications · 2023Article
- Review
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)-a multivariate method developed for econometrics-to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.