ArticleBMC systems biology2015
The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.BMC systems biology · 2016Article
- Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics.BMC bioinformatics · 2015Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks--namely non-random degree distribution and high clustering coefficient--are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization.
methodsWe have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks.
resultsBy using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations.
conclusionsThe analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property.
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.