IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0273404.html
   My bibliography  Save this article

Optimization of parameters in coherent spin dynamics of radical pairs in quantum biology

Author

Listed:
  • Carlos F Martino
  • Pablo Jimenez
  • Max Goldfarb
  • Ugur G Abdulla

Abstract

Identification of the external electromagnetic fields and internal hyperfine parameters which optimize the quantum singlet-triplet yield of simplified radical pairs modeled by Schrödinger system with spin Hamiltonians given by the sum of Zeeman interaction and hyperfine coupling interaction terms are analyzed. A method that combines sensitivity analysis with Tikhonov regularization is implemented. Numerical results demonstrate that the quantum singlet-triplet yield of the radical pair system can be significantly reduced if optimization is pursued simultaneously for both external magnetic fields and internal hyperfine parameters. The results may contribute towards understanding the structure-function relationship of a putative magnetoreceptor to manipulate and enhance quantum coherences at room temperature and leveraging biofidelic function to inspire novel quantum devices.

Suggested Citation

  • Carlos F Martino & Pablo Jimenez & Max Goldfarb & Ugur G Abdulla, 2023. "Optimization of parameters in coherent spin dynamics of radical pairs in quantum biology," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-17, February.
    • Handle: RePEc:plo:pone00:0273404
    DOI: 10.1371/journal.pone.0273404
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0273404
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0273404&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0273404?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. Andreas Raue & Marcel Schilling & Julie Bachmann & Andrew Matteson & Max Schelke & Daniel Kaschek & Sabine Hug & Clemens Kreutz & Brian D Harms & Fabian J Theis & Ursula Klingmüller & Jens Timmer, 2013. "Lessons Learned from Quantitative Dynamical Modeling in Systems Biology," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-17, September.
    2. Philip Ball, 2011. "Physics of life: The dawn of quantum biology," Nature, Nature, vol. 474(7351), pages 272-274, June.
    Full references (including those not matched with items on IDEAS)

    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. Valdemar Melicher & Tom Haber & Wim Vanroose, 2017. "Fast derivatives of likelihood functionals for ODE based models using adjoint-state method," Computational Statistics, Springer, vol. 32(4), pages 1621-1643, December.
    2. Jin, Ding & Thube, Sneha Dattatraya & Hedtrich, Johannes & Henning, Christian & Delzeit, Ruth, 2019. "A Baseline Calibration Procedure for CGE models: An Application for DART," Conference papers 333057, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    3. Alonso-Sanz, Ramón & Situ, Haozhen, 2017. "A quantum relativistic battle of the sexes cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 267-277.
    4. Elba Raimúndez & Simone Keller & Gwen Zwingenberger & Karolin Ebert & Sabine Hug & Fabian J Theis & Dieter Maier & Birgit Luber & Jan Hasenauer, 2020. "Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-21, March.
    5. repec:plo:pcbi00:1005234 is not listed on IDEAS
    6. Fabian Fröhlich & Barbara Kaltenbacher & Fabian J Theis & Jan Hasenauer, 2017. "Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-18, January.
    7. Diane Lefaudeux & Supriya Sen & Kevin Jiang & Alexander Hoffmann, 2022. "Kinetics of mRNA nuclear export regulate innate immune response gene expression," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    8. Neveen Ali Eshtewy & Lena Scholz & Andreas Kremling, 2023. "Parameter Estimation for a Kinetic Model of a Cellular System Using Model Order Reduction Method," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    9. Nathaniel J Linden & Boris Kramer & Padmini Rangamani, 2022. "Bayesian parameter estimation for dynamical models in systems biology," PLOS Computational Biology, Public Library of Science, vol. 18(10), pages 1-48, October.
    10. Eivind S Haus & Tormod Drengstig & Kristian Thorsen, 2023. "Structural identifiability of biomolecular controller motifs with and without flow measurements as model output," PLOS Computational Biology, Public Library of Science, vol. 19(8), pages 1-33, August.
    11. Fabian Fröhlich & Philipp Thomas & Atefeh Kazeroonian & Fabian J Theis & Ramon Grima & Jan Hasenauer, 2016. "Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-28, July.
    12. Zijian Wang & Jan Hasenauer & Yannik Schälte, 2024. "Missing data in amortized simulation-based neural posterior estimation," PLOS Computational Biology, Public Library of Science, vol. 20(6), pages 1-17, June.
    13. Polina Lakrisenko & Paul Stapor & Stephan Grein & Łukasz Paszkowski & Dilan Pathirana & Fabian Fröhlich & Glenn Terje Lines & Daniel Weindl & Jan Hasenauer, 2023. "Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks," PLOS Computational Biology, Public Library of Science, vol. 19(1), pages 1-19, January.

    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:pone00:0273404. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.