Evidence map›Paper›PMID 32978682›Full record

ArticleBulletin of mathematical biology2020

Large-Scale Dynamics of Self-propelled Particles Moving Through Obstacles: Model Derivation and Pattern Formation.

P Aceves-Sanchez, P Degond, E E Keaveny, A Manhart, S Merino-Aceituno, D Peurichard

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bulletin of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

P Aceves-SanchezDepartment of Mathematics, North Carolina State University, Raleigh, NC, 27695, USA.
P DegondDepartment of Mathematics, Imperial College London, London, SW7 2AZ, UK.
E E KeavenyDepartment of Mathematics, Imperial College London, London, SW7 2AZ, UK.
A ManhartDepartment of Mathematics, University College London, London, WC1H 0AY, United Kingdom. a.manhart@ucl.ac.uk.ORCID 0000-0002-0303-8114
S Merino-AceitunoFaculty for Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090, Vienna, Austria.
D PeurichardInria Paris - MAMBA Project Team, Laboratory Jacques-Louis Lions, Sorbonne University, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We model and study the patterns created through the interaction of collectively moving self-propelled particles (SPPs) and elastically tethered obstacles. Simulations of an individual-based model reveal at least three distinct large-scale patterns: travelling bands, trails and moving clusters. This motivates the derivation of a macroscopic partial differential equations model for the interactions between the self-propelled particles and the obstacles, for which we assume large tether stiffness. The result is a coupled system of nonlinear, non-local partial differential equations. Linear stability analysis shows that patterning is expected if the interactions are strong enough and allows for the predictions of pattern size from model parameters. The macroscopic equations reveal that the obstacle interactions induce short-ranged SPP aggregation, irrespective of whether obstacles and SPPs are attractive or repulsive.

Indexed as

Models, TheoreticalMolecular Dynamics SimulationMathematical ConceptsParticle SizeGradient flowHydrodynamic limitNon-local interactionsPattern formationSelf-propelled particlesStability analysis

Identifiers

PMID32978682
PMCPMC7519010

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.