IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0031657.html
   My bibliography  Save this article

Adaptive Models for Gene Networks

Author

Listed:
  • Yong-Jun Shin
  • Ali H Sayed
  • Xiling Shen

Abstract

Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.

Suggested Citation

  • Yong-Jun Shin & Ali H Sayed & Xiling Shen, 2012. "Adaptive Models for Gene Networks," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-6, February.
    • Handle: RePEc:plo:pone00:0031657
    DOI: 10.1371/journal.pone.0031657
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0031657
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0031657&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0031657?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. Bert Vogelstein & David Lane & Arnold J. Levine, 2000. "Surfing the p53 network," Nature, Nature, vol. 408(6810), pages 307-310, November.
    2. Ivan N. Colaluca & Daniela Tosoni & Paolo Nuciforo & Francesca Senic-Matuglia & Viviana Galimberti & Giuseppe Viale & Salvatore Pece & Pier Paolo Di Fiore, 2008. "NUMB controls p53 tumour suppressor activity," Nature, Nature, vol. 451(7174), pages 76-80, January.
    3. Xiaodian Sun & Li Jin & Momiao Xiong, 2008. "Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks," PLOS ONE, Public Library of Science, vol. 3(11), pages 1-13, November.
    4. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
    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. Afnizanfaizal Abdullah & Safaai Deris & Mohd Saberi Mohamad & Sohail Anwar, 2013. "An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    2. Takanori Hasegawa & Rui Yamaguchi & Masao Nagasaki & Satoru Miyano & Seiya Imoto, 2014. "Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-19, August.
    3. Bálint Mészáros & István Simon & Zsuzsanna Dosztányi, 2009. "Prediction of Protein Binding Regions in Disordered Proteins," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    4. Jae Kyoung Kim & Trachette L Jackson, 2013. "Mechanisms That Enhance Sustainability of p53 Pulses," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    5. Piotrowska, Monika J. & Bartłomiejczyk, Agnieszka & Bodnar, Marek, 2018. "Mathematical analysis of a generalised p53-Mdm2 protein gene expression model," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 26-44.
    6. Alireza Yazdani & Lu Lu & Maziar Raissi & George Em Karniadakis, 2020. "Systems biology informed deep learning for inferring parameters and hidden dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-19, November.
    7. Jeffrey V Wong & Bochong Li & Lingchong You, 2012. "Tension and Robustness in Multitasking Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-12, April.
    8. Wassim Abou-Jaoudé & Madalena Chaves & Jean-Luc Gouzé, 2011. "A Theoretical Exploration of Birhythmicity in the p53-Mdm2 Network," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-12, February.
    9. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
    10. Joseph D Taylor & Samuel Winnall & Alain Nogaret, 2020. "Estimation of neuron parameters from imperfect observations," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-22, July.
    11. Dimitrios V Vavoulis & Volko A Straub & John A D Aston & Jianfeng Feng, 2012. "A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-1, March.
    12. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.
    13. Fuaada Mohd Siam & Muhamad Hanis Nasir, 2019. "Comparison of parameter fitting on the model of irradiation effects on bystander cells between Nelder-Mead simplex and particle swarm optimization," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 5(3), pages 142-150.
    14. Sungho Shin & Ophelia S Venturelli & Victor M Zavala, 2019. "Scalable nonlinear programming framework for parameter estimation in dynamic biological system models," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-29, March.
    15. Tian Ge & Keith M Kendrick & Jianfeng Feng, 2009. "A Novel Extended Granger Causal Model Approach Demonstrates Brain Hemispheric Differences during Face Recognition Learning," PLOS Computational Biology, Public Library of Science, vol. 5(11), pages 1-13, November.
    16. Agus Hartoyo & Peter J Cadusch & David T J Liley & Damien G Hicks, 2019. "Parameter estimation and identifiability in a neural population model for electro-cortical activity," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-27, May.
    17. Se Ho Park & Seokmin Ha & Jae Kyoung Kim, 2023. "A general model-based causal inference method overcomes the curse of synchrony and indirect effect," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    18. Phillips, J.C., 2016. "Autoantibody recognition mechanisms of p53 epitopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 162-170.
    19. Zhilei Ge & Suyun Liu & Guopeng Li & Yan Huang & Yanni Wang, 2017. "Error model of geomagnetic-field measurement and extended Kalman-filter based compensation method," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
    20. González Javier & Vujačić Ivan & Wit Ernst, 2013. "Inferring latent gene regulatory network kinetics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(1), pages 109-127, March.

    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:pone00:0031657. 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.