ArticleNature communications2016
A network property necessary for concentration robustness.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Topological criterion for robust perfect adaptation of reaction fluxes in biological networks.iScience · 2025Article
- Universal structures for adaptation in biochemical reaction networks.Nature communications · 2023Article
- Dark accumulation of downstream glycolytic intermediates initiates robust photosynthesis in cyanobacteria.Plant physiology · 2023Article
- Development of a novel anti-liver fibrosis formula with luteolin, licochalcone A, aloe-emodin and acacetin by network pharmacology and transcriptomics analysis.Pharmaceutical biology · 2021Article
- Coupling effect of nodes popularity and similarity on social network persistence.Scientific reports · 2017Article
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Authors and funding
7 authors.
Funding
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Abstract
Maintenance of functionality of complex cellular networks and entire organisms exposed to environmental perturbations often depends on concentration robustness of the underlying components. Yet, the reasons and consequences of concentration robustness in large-scale cellular networks remain largely unknown. Here, we derive a necessary condition for concentration robustness based only on the structure of networks endowed with mass action kinetics. The structural condition can be used to design targeted experiments to study concentration robustness. We show that metabolites satisfying the necessary condition are present in metabolic networks from diverse species, suggesting prevalence of this property across kingdoms of life. We also demonstrate that our predictions about concentration robustness of energy-related metabolites are in line with experimental evidence from Escherichia coli. The necessary condition is applicable to mass action biological systems of arbitrary size, and will enable understanding the implications of concentration robustness in genetic engineering strategies and medical applications.
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