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The positive role of noise for information acquisition in biological signaling pathways
Open accessPreprint — published as 10.1186/s12859-020-03778-x · Paper page
Abstract
All living systems acquire information about their environment. At the cellular level, they do so through signaling pathways, which rely on interactions between molecules that detect and transmit the presence of an extracellular cue or signal to the cell’s interior. Such interactions are inherently stochastic and thus noisy. In classical information theory, a noisy communication channel degrades the amount of transmissible information relative to a noise-free channel. For this reason, one would expect that the kinetic parameters that determine a pathway’s operation minimize noise. We show that this is not the case under a wide range of biologically sensible parameter values. Specifically, we perform computational simulations of simple signaling systems, which show that a noisy molecular interaction dynamics is a necessary condition for information acquisition. Moreover, we show that optimal information acquisition, where a system reacts most sensitively to changes in the environment, can be obtained close to the maximal attainable level of noise in the system. Our work highlights the positive role that noise can have in biological information processing. Author summary: The acquisition of information is fundamental for living systems, because the decisions they take based on such information directly affect survival and reproduction. The molecular mechanisms used by cells to acquire information are signaling pathways. The molecular interactions of signaling pathways, such as the binding of a signal to a receptor, are by nature noisy. This is important, because noise disrupts information. Hence, to maximize the acquisition of information, signaling pathways should minimize the noise of their molecular interactions. Here we show that a noisy dynamic of the molecular interactions can improve the acquisition of information, and that the maximal capacity to acquire information can be obtained with a close-to-maximal level of noise in a signaling pathway. Thus, contrary to expectations, noise can improve the acquisition of information in living systems.
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Metadata
- Authors
- Azpeitia, E., & Wagner, A.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Wagner, Andreas
- Venue
- BMC Bioinformatics
- Publisher
- openRxiv
- Published
- 2019-09-09
- Volume
- 21
- Issue
- 1
- Pages
- 462-462
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC7568421
- OpenAlex
- W3092838074
- Cited by
- 20
- References
- 1
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 2 · 2022: 3 · 2021: 2 · 2020: 2
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation (31003A_172887)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (739874, 172887)
- H2020 European Research Council (739874)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Advanced Thermodynamics and Statistical Mechanics
- Keywords
- signaling pathways, noise, information acquisition, stochastic resonance, kinetic parameters, signal detection, stochastic dynamics
- MeSH terms
- Gene Expression Regulation, Kinetics, Ligands, Models, Biological, Signal Transduction
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