ArticleNature communications2015
The limits of chemosensation vary across dimensions.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- ChemotaxingArXiv · 2025Article
- ChemotaxingbioRxiv : the preprint server for biology · 2025Article
- Learning stochastic reaction-diffusion models from limited data using spatiotemporal features.bioRxiv : the preprint server for biology · 2025Article
- Limits on the accuracy of contact inhibition of locomotion.Physical review. E · 2024Article
- Origins of eukaryotic excitability.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Review
- Current understanding of biological identity at the nanoscale and future prospects.Nature nanotechnology · 2021Review
- High-throughput three-dimensional chemotactic assays reveal steepness-dependent complexity in neuronal sensation to molecular gradients.Nature communications · 2018Article
- Theoretical Models of Neural Development.iScience · 2018Review
- Control of neurite growth and guidance by an inhibitory cell-body signal.PLoS computational biology · 2018Article
- Reconstructing the gradient source position from steady-state fluxes to small receptors.Scientific reports · 2018Article
- Collective Chemotaxis through Noisy Multicellular Gradient Sensing.Biophysical journal · 2016Article
- Cox process representation and inference for stochastic reaction-diffusion processes.Nature communications · 2016Article
- Extending the dynamic range of transcription factor action by translational regulation.Physical review. E · 2016Article
- Know the Single-Receptor Sensing Limit? Think Again.Journal of statistical physicsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Many biological processes rely on the ability of cells to measure local ligand concentration. However, such measurements are constrained by noise arising from diffusion and the stochastic nature of receptor-ligand interactions. It is thus critical to understand how accurately, in principle, concentration measurements can be made. Previous theoretical work has mostly investigated this in 3D under the simplifying assumption of an unbounded domain of diffusion, but many biological problems involve 2D concentration measurement in bounded domains, for which diffusion behaves quite differently. Here we present a theory of the precision of chemosensation that covers bounded domains of any dimensionality. We find that the quality of chemosensation in lower dimensions is controlled by domain size, suggesting a general principle applicable to many biological systems. Applying the theory to biological problems in 2D shows that diffusion-limited signalling is an efficient mechanism on time scales consistent with behaviour.
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.