ArticlePLoS computational biology2015
Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome.
Article in PLoS computational biology, 2015. 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
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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.
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Who cites it
71 citing papers in PubMed, 139 citations in OpenAlex.
- The FERM guild: a differentially correlated microbial module drives hypertension via metabolic flux perturbations.mSystems · 2026Article
- Changes in anorectal and oropharyngeal microbiota following infection of Neisseria gonorrhoeae in men who have sex with men: a prospective cohort study.BMC microbiology · 2026Article
- 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
- Experimental systems are essential for strengthening ecological modeling of microbiomes beyond observational data.mSystems · 2025Review
- Random walk based snapshot clustering for detecting community dynamics in temporal networks.Scientific reports · 2025Article
- A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.Statistics in medicine · 2025Article
- Integrating network analysis with in vivo-in situ manipulation to elucidate microbiome dynamics in Aedes albopictus.FEMS microbiology letters · 2025Article
- The Interactive Effects of Nutrient Density and Breed on Growth Performance and Gut Microbiota in Broilers.Animals : an open access journal from MDPI · 2024Article
- Article
- Measuring and understanding information storage and transfer in a simulated human gut microbiome.PLoS computational biology · 2024Article
- Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.IEEE transactions on artificial intelligence · 2024Article
- Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024Review
- Models of Cell Processes are Far from the Edge of Chaos.PRX life · 2023Article
- Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome.Scientific reports · 2023Article
- Probiotics: insights and new opportunities forCritical reviews in microbiology · 2023Review
- Optimal Recursive Expert-Enabled Inference in Regulatory Networks.IEEE control systems letters · 2023Article
- Data-driven learning of Boolean networks and functions by optimal causation entropy principle.Patterns (New York, N.Y.) · 2022Article
- Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks.Scientific reports · 2022Article
- Complexity-stability trade-off in empirical microbial ecosystems.Nature ecology & evolution · 2022Article
- Productive Performance, Serum Antioxidant Status, Tissue Selenium Deposition, and Gut Health Analysis of Broiler Chickens Supplemented with Selenium and Probiotics-A Pilot Study.Animals : an open access journal from MDPI · 2022Article
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
5 authors at 3 institutions in 1 country.
Funding
Abstract
We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.
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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.