IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000764.html
   My bibliography  Save this article

A Critical Quantity for Noise Attenuation in Feedback Systems

Author

Listed:
  • Liming Wang
  • Jack Xin
  • Qing Nie

Abstract

Feedback modules, which appear ubiquitously in biological regulations, are often subject to disturbances from the input, leading to fluctuations in the output. Thus, the question becomes how a feedback system can produce a faithful response with a noisy input. We employed multiple time scale analysis, Fluctuation Dissipation Theorem, linear stability, and numerical simulations to investigate a module with one positive feedback loop driven by an external stimulus, and we obtained a critical quantity in noise attenuation, termed as “signed activation time”. We then studied the signed activation time for a system of two positive feedback loops, a system of one positive feedback loop and one negative feedback loop, and six other existing biological models consisting of multiple components along with positive and negative feedback loops. An inverse relationship is found between the noise amplification rate and the signed activation time, defined as the difference between the deactivation and activation time scales of the noise-free system, normalized by the frequency of noises presented in the input. Thus, the combination of fast activation and slow deactivation provides the best noise attenuation, and it can be attained in a single positive feedback loop system. An additional positive feedback loop often leads to a marked decrease in activation time, decrease or slight increase of deactivation time and allows larger kinetic rate variations for slow deactivation and fast activation. On the other hand, a negative feedback loop may increase the activation and deactivation times. The negative relationship between the noise amplification rate and the signed activation time also holds for the six other biological models with multiple components and feedback loops. This principle may be applicable to other feedback systems.Author Summary: Many biological systems use feedback loops to regulate dynamic interactions among different genes and proteins. Here, we ask how interlinked feedback loops control the timing of signal transductions and responses and, consequently, attenuate noise. Drawing on simple modeling along with both analytical insights and computational assessments, we have identified a key quantity, termed as the “signed activation time”, that dictates a system's ability of attenuating noise. This quantity combining the speed of deactivation and activation in signal responses, relative to the input noise frequency, is determined by the property of feedback systems when noises are absent. In general, such quantity could be measured experimentally through the output response time of a signaling system driven by pulse stimulus. This principle for noise attenuation in feedback loops may also be applicable to other biological systems involving more complex regulations.

Suggested Citation

  • Liming Wang & Jack Xin & Qing Nie, 2010. "A Critical Quantity for Noise Attenuation in Feedback Systems," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-17, April.
  • Handle: RePEc:plo:pcbi00:1000764
    DOI: 10.1371/journal.pcbi.1000764
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000764
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000764&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1000764?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Attila Becskei & Luis Serrano, 2000. "Engineering stability in gene networks by autoregulation," Nature, Nature, vol. 405(6786), pages 590-593, June.
    2. Gil Hornung & Naama Barkai, 2008. "Noise Propagation and Signaling Sensitivity in Biological Networks: A Role for Positive Feedback," PLOS Computational Biology, Public Library of Science, vol. 4(1), pages 1-7, January.
    3. Keun-Young Kim & Jin Wang, 2007. "Potential Energy Landscape and Robustness of a Gene Regulatory Network: Toggle Switch," PLOS Computational Biology, Public Library of Science, vol. 3(3), pages 1-13, March.
    4. Carlos Gomez-Uribe & George C Verghese & Leonid A Mirny, 2007. "Operating Regimes of Signaling Cycles: Statics, Dynamics, and Noise Filtering," PLOS Computational Biology, Public Library of Science, vol. 3(12), pages 1-11, December.
    5. Lufen Chang & Michael Karin, 2001. "Mammalian MAP kinase signalling cascades," Nature, Nature, vol. 410(6824), pages 37-40, March.
    6. Matthew Freeman, 2000. "Feedback control of intercellular signalling in development," Nature, Nature, vol. 408(6810), pages 313-319, November.
    7. Steven J. Altschuler & Sigurd B. Angenent & Yanqin Wang & Lani F. Wu, 2008. "On the spontaneous emergence of cell polarity," Nature, Nature, vol. 454(7206), pages 886-889, August.
    8. Johan Paulsson, 2004. "Summing up the noise in gene networks," Nature, Nature, vol. 427(6973), pages 415-418, January.
    9. Ching-Shan Chou & Qing Nie & Tau-Mu Yi, 2008. "Modeling Robustness Tradeoffs in Yeast Cell Polarization Induced by Spatial Gradients," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-16, September.
    10. Christopher V. Rao & Denise M. Wolf & Adam P. Arkin, 2002. "Control, exploitation and tolerance of intracellular noise," Nature, Nature, vol. 420(6912), pages 231-237, November.
    11. D. W. Austin & M. S. Allen & J. M. McCollum & R. D. Dar & J. R. Wilgus & G. S. Sayler & N. F. Samatova & C. D. Cox & M. L. Simpson, 2006. "Gene network shaping of inherent noise spectra," Nature, Nature, vol. 439(7076), pages 608-611, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alexandra Jilkine & Sigurd B Angenent & Lani F Wu & Steven J Altschuler, 2011. "A Density-Dependent Switch Drives Stochastic Clustering and Polarization of Signaling Molecules," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-11, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yichen Li & Yumin Li & Hui Zhang & Yong Chen, 2011. "MicroRNA-Mediated Positive Feedback Loop and Optimized Bistable Switch in a Cancer Network Involving miR-17-92," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-9, October.
    2. Frank J Bruggeman & Nils Blüthgen & Hans V Westerhoff, 2009. "Noise Management by Molecular Networks," PLOS Computational Biology, Public Library of Science, vol. 5(9), pages 1-11, September.
    3. Hui Zhang & Yueling Chen & Yong Chen, 2012. "Noise Propagation in Gene Regulation Networks Involving Interlinked Positive and Negative Feedback Loops," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    4. Kyung H Kim & Herbert M Sauro, 2012. "Adjusting Phenotypes by Noise Control," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-14, January.
    5. Tanya L Leise & Connie W Wang & Paula J Gitis & David K Welsh, 2012. "Persistent Cell-Autonomous Circadian Oscillations in Fibroblasts Revealed by Six-Week Single-Cell Imaging of PER2::LUC Bioluminescence," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    6. Raul Fernandez-Lopez & Irene del Campo & Carlos Revilla & Ana Cuevas & Fernando de la Cruz, 2014. "Negative Feedback and Transcriptional Overshooting in a Regulatory Network for Horizontal Gene Transfer," PLOS Genetics, Public Library of Science, vol. 10(2), pages 1-15, February.
    7. Luca Cardelli & Rosa D Hernansaiz-Ballesteros & Neil Dalchau & Attila Csikász-Nagy, 2017. "Efficient Switches in Biology and Computer Science," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-16, January.
    8. Zhou, Peipei & Cai, Shuiming & Liu, Zengrong & Chen, Luonan & Wang, Ruiqi, 2013. "Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems," Chaos, Solitons & Fractals, Elsevier, vol. 50(C), pages 115-126.
    9. Mark Hallen & Bochong Li & Yu Tanouchi & Cheemeng Tan & Mike West & Lingchong You, 2011. "Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-16, October.
    10. Michael Trogdon & Brian Drawert & Carlos Gomez & Samhita P Banavar & Tau-Mu Yi & Otger Campàs & Linda R Petzold, 2018. "The effect of cell geometry on polarization in budding yeast," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-22, June.
    11. Abhyudai Singh & Mohammad Soltani, 2013. "Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    12. Tina Toni & Bruce Tidor, 2013. "Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-17, March.
    13. Rutger Hermsen & Bas Ursem & Pieter Rein ten Wolde, 2010. "Combinatorial Gene Regulation Using Auto-Regulation," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-13, June.
    14. Stefano Ciliberti & Olivier C Martin & Andreas Wagner, 2007. "Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology," PLOS Computational Biology, Public Library of Science, vol. 3(2), pages 1-10, February.
    15. Benjamin B Kaufmann & Qiong Yang & Jerome T Mettetal & Alexander van Oudenaarden, 2007. "Heritable Stochastic Switching Revealed by Single-Cell Genealogy," PLOS Biology, Public Library of Science, vol. 5(9), pages 1-8, September.
    16. Jin Wang & Bo Huang & Xuefeng Xia & Zhirong Sun, 2006. "Funneled Landscape Leads to Robustness of Cell Networks: Yeast Cell Cycle," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-10, November.
    17. Silke Neumann & Linda Løvdok & Kajetan Bentele & Johannes Meisig & Ekkehard Ullner & Ferencz S Paldy & Victor Sourjik & Markus Kollmann, 2014. "Exponential Signaling Gain at the Receptor Level Enhances Signal-to-Noise Ratio in Bacterial Chemotaxis," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-11, April.
    18. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    19. Mayu Sugiyama & Takashi Saitou & Hiroshi Kurokawa & Asako Sakaue-Sawano & Takeshi Imamura & Atsushi Miyawaki & Tadahiro Iimura, 2014. "Live Imaging-Based Model Selection Reveals Periodic Regulation of the Stochastic G1/S Phase Transition in Vertebrate Axial Development," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-16, December.
    20. Yurie Okabe-Oho & Hiroki Murakami & Suguru Oho & Masaki Sasai, 2009. "Stable, Precise, and Reproducible Patterning of Bicoid and Hunchback Molecules in the Early Drosophila Embryo," PLOS Computational Biology, Public Library of Science, vol. 5(8), pages 1-20, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1000764. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.