Evidence map›Paper›PMID 28951597›Full record

ArticleScientific reports2017

Explorability and the origin of network sparsity in living systems.

Daniel M Busiello, Samir Suweis, Jorge Hidalgo, Amos Maritan

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  7. Scaling approaches and macroecology provide a foundation for assessing ecological resilience in the Anthropocene.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article
  8. Network Statistics of the Whole-Brain Connectome ofbioRxiv : the preprint server for biology · 2024
    Article
  9. Sparse species interactions reproduce abundance correlation patterns in microbial communities.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

4 authors.

Daniel M BusielloDepartment of Physics and Astronomy, University of Padova, CNISM and INFN, 35131, Padova, Italy.
Samir SuweisDepartment of Physics and Astronomy, University of Padova, CNISM and INFN, 35131, Padova, Italy.
Jorge HidalgoDepartment of Physics and Astronomy, University of Padova, CNISM and INFN, 35131, Padova, Italy.
Amos MaritanDepartment of Physics and Astronomy, University of Padova, CNISM and INFN, 35131, Padova, Italy. amos.maritan@pd.infn.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing volume of ecologically and biologically relevant data has revealed a wide collection of emergent patterns in living systems. Analysing different data sets, ranging from metabolic gene-regulatory to species interaction networks, we find that these networks are sparse, i.e. the percentage of the active interactions scales inversely proportional to the system size. To explain the origin of this puzzling common characteristic, we introduce the new concept of explorability: a measure of the ability of an interacting system to adapt to newly intervening changes. We show that sparsity is an emergent property resulting from optimising both explorability and dynamical robustness, i.e. the capacity of the system to remain stable after perturbations of the underlying dynamics. Networks with higher connectivities lead to an incremental difficulty to find better values for both the explorability and dynamical robustness, associated with the fine-tuning of the newly added interactions. A relevant characteristic of our solution is its scale invariance, i.e., it remains optimal when several communities are assembled together. Connectivity is also a key ingredient in determining ecosystem stability and our proposed solution contributes to solving May's celebrated complexity-stability paradox.

Indexed as

EcosystemModels, BiologicalComputer Simulation

Identifiers

PMID28951597
PMCPMC5615038

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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.