ArticleNature2010
Fundamental limits on the suppression of molecular fluctuations.
Article in Nature, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers.
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Who cites it
170 citing papers in PubMed, 457 citations in OpenAlex.
- GenAI-Net: A generative AI framework for automated biomolecular network design.Science advances · 2026Article
- A fundamental trade-off among noise suppression, response sensitivity and speed in biological feedback networks.NPJ systems biology and applications · 2026Article
- Stochastic Gene Expression Model with State-Dependent Protein Activation Delay.bioRxiv : the preprint server for biology · 2026Article
- When prediction fails: a computational complexity view of stress.Frontiers in veterinary science · 2026Article
- Toward single-cell control: noise-robust perfect adaptation in biomolecular systems.Nature communications · 2025Article
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- Positioning of negative feedback loops within immune signaling pathways influences gene expression noise.bioRxiv : the preprint server for biology · 2025Article
- Controlling noise with self-organized resetting.Communications physics · 2025Article
- Histone deacetylase Hos2 regulates protein expression noise by potentially modulating the protein translation machinery.Nucleic acids research · 2024Article
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- Understanding Cell Differentiation Through Single-Cell Approaches: Conceptual Challenges of the Systemic Approach.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Actomyosin pulsing rescues embryonic tissue folding from disruption by myosin fluctuations.Research square · 2023Article
- Noise properties of adaptation-conferring biochemical control modules.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Improved approaches to channel capacity estimation discover compromised GPCR signaling in diverse cancer cells.iScience · 2023Article
- A synthetic population-level oscillator in non-microfluidic environments.Communications biology · 2023Article
- An optimized reporter of the transcription factor hypoxia-inducible factor 1α reveals complex HIF-1α activation dynamics in single cells.The Journal of biological chemistry · 2023Article
- Localization of Noise in Biochemical Networks.ACS omega · 2023Article
- Molecular Origins of Transcriptional Heterogeneity in Diazotrophic Klebsiella oxytoca.mSystems · 2022Article
- Distributed information encoding and decoding using self-organized spatial patterns.Patterns (New York, N.Y.) · 2022Article
- Efficient information coding and degeneracy in the nervous system.Current opinion in neurobiology · 2022Review
110 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Negative feedback is common in biological processes and can increase a system's stability to internal and external perturbations. But at the molecular level, control loops always involve signalling steps with finite rates for random births and deaths of individual molecules. Here we show, by developing mathematical tools that merge control and information theory with physical chemistry, that seemingly mild constraints on these rates place severe limits on the ability to suppress molecular fluctuations. Specifically, the minimum standard deviation in abundances decreases with the quartic root of the number of signalling events, making it extremely expensive to increase accuracy. Our results are formulated in terms of experimental observables, and existing data show that cells use brute force when noise suppression is essential; for example, regulatory genes are transcribed tens of thousands of times per cell cycle. The theory challenges conventional beliefs about biochemical accuracy and presents an approach to the rigorous analysis of poorly characterized biological systems.
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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.