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Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression

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  • Bryan C Daniels
  • Ilya Nemenman

Abstract

The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem.

Suggested Citation

  • Bryan C Daniels & Ilya Nemenman, 2015. "Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
  • Handle: RePEc:plo:pone00:0119821
    DOI: 10.1371/journal.pone.0119821
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    References listed on IDEAS

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    1. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
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    Cited by:

    1. Aguilar-Canto, Fernando Javier & Brito-Loeza, Carlos & Calvo, Hiram, 2024. "Model discovery of compartmental models with Graph-Supported Neural Networks," Applied Mathematics and Computation, Elsevier, vol. 464(C).
    2. Mark K Transtrum & Peng Qiu, 2016. "Bridging Mechanistic and Phenomenological Models of Complex Biological Systems," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-34, May.
    3. 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.

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