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Adaptive Models for Gene Networks

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  • 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
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    References listed on IDEAS

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    1. Bert Vogelstein & David Lane & Arnold J. Levine, 2000. "Surfing the p53 network," Nature, Nature, vol. 408(6810), pages 307-310, November.
    2. 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.
    3. 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.
    4. 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.
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