ReviewNature microbiology2019
Modelling approaches for studying the microbiome.
Review in Nature microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 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
90 citing papers in PubMed.
- Comparative insights into the gut-heart axis: cross-species and cross-population perspectives.Gut microbes · 2026Review
- Modelling the role of the microbiome in antimicrobial resistance across scales.Nature microbiology · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- Review
- Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.Nature communications · 2026Article
- Water kefir as a paradigm for multi-omics and genome-scale metabolic modelling in fermented food.NPJ biofilms and microbiomes · 2026Review
- Modulating microbial intake helps to maintain the gut microbiome diversity.Communications biology · 2026Article
- Identification of bacterial candidates that promote the growth of the seagrassbioRxiv : the preprint server for biology · 2026Article
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- Viromics approaches for the study of viral diversity and ecology in microbiomes.Nature reviews. Genetics · 2026Review
- SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025Article
- Review
- The influence of environment on bacterial co-abundance in the gut microbiomes of healthy human individuals.Communications biology · 2025Article
- hiPSCGEM01: A Genome-Scale Metabolic Model for Fibroblast-Derived Human iPSCs.Bioengineering (Basel, Switzerland) · 2025Article
- 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
- Deciphering dynamic antibiotics-microbiome-metabolome interactions in preterm infants using systems biology.iScience · 2025Article
- Exploring the Biosynthetic Potential of Microorganisms from the South China Sea Cold Seep Using Culture-Dependent and Culture-Independent Approaches.Marine drugs · 2025Article
- Multi-omics analysis reveals gut microbiota-metabolite interactions and their association with liver function in autoimmune overlap syndrome.World journal of gastroenterology · 2025Article
- Agent-based model of the human colon to investigate mechanisms of pathogen colonization resistance.Biophysical journal · 2025Article
- Complex system modeling reveals oxalate homeostasis is driven by diverse oxalate-degrading bacteria.eLife · 2025Article
30 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
No grant is acknowledged in the PubMed record.
Abstract
Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health.
Indexed as
Identifiers
31337891What 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.