ArticleProceedings of the National Academy of Sciences of the United States of America2017
The topography of the environment alters the optimal search strategy for active particles.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Active Particles in Tunable Compressible Environments.Small science · 2026Article
- Perspective on "Active Brownian particles moving in a random Lorentz gas".The European physical journal. E, Soft matter · 2026Article
- Article
- Anomalous Dynamics of Superparamagnetic Colloidal Microrobots with Tailored Statistics.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Liver sinusoid constraints make random search of CD8 T cells for Plasmodium parasites efficient.bioRxiv : the preprint server for biology · 2025Article
- Coarse-graining bacterial diffusion in disordered media to surface states.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Swarm Autonomy: From Agent Functionalization to Machine Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Smart active particles learn and transcend bacterial foraging strategies.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Microbes in porous environments: from active interactions to emergent feedback.Biophysical reviews · 2024Review
- Learning how to find targets in the micro-world: the case of intermittent active Brownian particles.Soft matter · 2024Article
- Optimal navigation of a smart active particle: directional and distance sensing.The European physical journal. E, Soft matter · 2023Article
- Survival strategies of artificial active agents.Scientific reports · 2023Article
- Brain-localized CD4 and CD8 T cells perform correlated random walks and not Levy walks.F1000Research · 2023Article
- Chemotactic self-caging in active emulsions.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Liver Environment-Imposed Constraints Diversify Movement Strategies of Liver-Localized CD8 T Cells.Journal of immunology (Baltimore, Md. : 1950) · 2022Article
- Computational modelling of cell motility modes emerging from cell-matrix adhesion dynamics.PLoS computational biology · 2022Article
- Collective self-optimization of communicating active particles.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A geometric criterion for the optimal spreading of active polymers in porous media.Nature communications · 2021Article
- The control effort to steer self-propelled microswimmers depends on their morphology: comparing symmetric spherical versus asymmetricRoyal Society open science · 2021Article
- The environment topography alters the way to multicellularity inScience advances · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search and rescue operations in disaster zones, and to genetic computer algorithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent [Formula: see text] are generally believed to optimize the search process. However, most search spaces present complex topographies. What is the best search strategy in these more realistic scenarios? Here, we show that the topography of the environment significantly alters the optimal search strategy toward less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for nonregenerating sparse targets in a 2D space with steps drawn from a Lévy distribution with the exponent varying from [Formula: see text] to [Formula: see text] (Brownian). We show that, when boundaries, barriers, and obstacles are present, the optimal search strategy depends on the topography of the environment, with [Formula: see text] assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher's size. Our results are relevant for search problems at different length scales from animal and human foraging to microswimmers' taxis to biochemical rates of reaction.
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.