Set-based parameter estimation for symmetric network motifs

P. Rumschinski, Dina Shona Laila, R. Findeisen

    Research output: Contribution to conferencePaper

    1 Citation (Scopus)


    Deriving a predictive model in systems biology is a complex task. One major problem is the typically large network size, which renders the analysis with standard methods difficult. Symmetry, as omnipresent in nature, was used in many applications to encounter this problem. In this work, we investigate the influence of symmetry on set-based parameter estimation. We show that the presence of symmetry in a model can be used to significantly simplify the parameter estimation problem. This is done by determining a symmetry-adapted basis, corresponding to a linear representation of a finite group, in which the problem size is of smaller dimension. We demonstrate the applicability of this approach for several common network motifs, as e.g. the Michaelis-Menten reaction and the feedforward motif.
    Original languageEnglish
    Publication statusPublished - 2011
    EventIFAC World Congress - Milan, Italy
    Duration: 28 Aug 20112 Sept 2011


    ConferenceIFAC World Congress

    Bibliographical note

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    • Parameter Estimation
    • Systems Biology
    • Symmetry


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