Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression

Bryan C. Daniels, Ilya Nemenman

    Research output: Contribution to journalArticle

    21 Scopus citations

    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.

    Original languageEnglish (US)
    Article numbere0119821
    JournalPloS one
    Volume10
    Issue number3
    DOIs
    StatePublished - Mar 25 2015

    ASJC Scopus subject areas

    • Biochemistry, Genetics and Molecular Biology(all)
    • Agricultural and Biological Sciences(all)
    • General

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