ReviewNature reviews. Microbiology2016
Advancing microbial sciences by individual-based modelling.
Review in Nature reviews. Microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 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
89 citing papers in PubMed, 205 citations in OpenAlex.
- Exploring novel kinetics of automated HMicrobiology spectrum · 2026Article
- Investigating the effects of soil microstructures on bacterial growth via microfluidic channels and an agent-based model.Scientific reports · 2025Article
- Dissecting the physics of bacterial biofilms with agent-based simulations.Current opinion in solid state & materials science · 2025Article
- Individual-based modeling unravels spatial and social interactions in bacterial communities.The ISME journal · 2025Review
- How to build the virtual cell with artificial intelligence: Priorities and opportunities.Cell · 2024Article
- An off-lattice discrete model to characterise filamentous yeast colony morphology.PLoS computational biology · 2024Article
- Bacterial community dynamics as a result of growth-yield trade-off and multispecies metabolic interactions toward understanding the gut biofilm niche.BMC microbiology · 2024Article
- How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.ArXiv · 2024Article
- Metabolic interactions shape emergent biofilm structures in a conceptual model of gut mucosal bacterial communities.NPJ biofilms and microbiomes · 2024Article
- Integrative analysis of yeast colony growth.Communications biology · 2024Article
- ABCD of IA: A multi-scale agent-based model of T cell activation in inflammatory arthritis.Biomaterials science · 2024Article
- Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0.PLoS computational biology · 2024Article
- Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.Microbial biotechnology · 2024Review
- Simultaneous spatiotemporal transcriptomics and microscopy of Bacillus subtilis swarm development reveal cooperation across generations.Nature microbiology · 2023Article
- Community composition and the environment modulate the population dynamics of type VI secretion in human gut bacteria.Nature ecology & evolution · 2023Article
- Article
- Community composition and the environment modulate the population dynamics of type VI secretion in human gut bacteria.bioRxiv : the preprint server for biology · 2023Article
- Controlling the human microbiome.Cell systems · 2023Review
- Article
- Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use.mLife · 2022Review
29 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 4 institutions in 3 countries.
Funding
Abstract
Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE).
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.