ArticleScientific reports2013
Stochastic effects as a force to increase the complexity of signaling networks.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Three systems of circuit formation: assembly, updating and tuning.Nature reviews. Neuroscience · 2025Review
- Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts.Trends in cell biology · 2022Review
- Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma.Biophysics reviews · 2022Review
- The partition representation of enzymatic reaction networks and its application for searching bi-stable reaction systems.PloS one · 2022Article
- Protein conformational dynamics and phenotypic switching.Biophysical reviews · 2021Review
- ACRE: Absolute concentration robustness exploration in module-based combinatorial networks.Synthetic biology (Oxford, England) · 2017Article
- Multiscale Modeling of Cellular Epigenetic States: Stochasticity in Molecular Networks, Chromatin Folding in Cell Nuclei, and Tissue Pattern Formation of Cells.Critical reviews in biomedical engineering · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism. However, the conditions under which molecular noise influences cellular information processing remain unclear. Here, we explore a large number of simple biological models of varying network sizes to understand the architectural conditions under which the interactions of signaling proteins can exhibit specific stochastic effects--called deviant effects--in which the average behavior of a biological system is substantially altered in the presence of molecular noise. We find that a small fraction of these networks does exhibit deviant effects and shares a common architectural feature whereas most of the networks show only insignificant levels of deviations. Interestingly, addition of seemingly unimportant interactions into protein networks gives rise to deviant effects.
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Registered trials
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