ArticlePLoS computational biology2013
Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 329 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
329 citing papers in PubMed, 675 citations in OpenAlex.
- Review
- Quantifying antibiotic susceptibility and inoculum effects using transient dynamics of Pseudomonas aeruginosa.PLoS computational biology · 2026Article
- Ecological Engineering of the Human Gut Microbiome: A Narrative Review and Framework for Next-Generation Therapeutics.Microorganisms · 2026Review
- Deciphering microbial community dynamics using cross-sectional data-informed NeuralODE.Microbiome · 2026Article
- Increased nutrient diversity can induce loss of microbial diversity through enhanced resource uptake.Nature ecology & evolution · 2026Article
- Reconstructing community dynamics from limited observations.Microbiome · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Dissimilarity measures for generalized Lotka-Volterra systems on networks.Scientific reports · 2026Article
- Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics.bioRxiv : the preprint server for biology · 2026Article
- The importance of regulated resource reallocation during dynamic environmental shifts in yeast.The EMBO journal · 2026Article
- Mathematical Modelling and Intuition in Microbiology: A Perspective.Environmental microbiology · 2026Review
- Emergent coexistence and the limits of reductionism in ecological communities.PLoS computational biology · 2026Article
- KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model.BioData mining · 2026Article
- Bayesian Pairwise Compositional Lotka-Volterra Modeling Infers Potential Rhizosphere Microbial Suppressors of Ralstonia pseudosolanacearum.The plant pathology journal · 2026Article
- A Predictive Computational Framework forPathogens (Basel, Switzerland) · 2026Article
- Stoichiometric analysis of microbial communities links function, structure, and biomass carrying capacity.The ISME journal · 2026Review
- Improving DirectLiNGAM for high-dimensional microbiome data: roots screening and eBIC based model selection.Frontiers in systems biology · 2026Article
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- gNODE: gLV model-informed neural ordinary differential equations for modeling microbial community dynamics.Frontiers in cellular and infection microbiology · 2026Article
- Longitudinal analysis reveals transitions in pathogen profiles associated with mastitis in dairy cows.Veterinary research · 2025Article
269 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
8 authors at 1 institution in 1 country.
Funding
Abstract
The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.
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.