Proximate parameter tuning for biochemical networks with uncertain kinetic parameters

Stephen J. Wilkinson, Neil Benson, Douglas B. Kell

    Research output: Contribution to journalArticlepeer-review

    Abstract

    It is commonly the case in biochemical modelling that we have knowledge of the qualitative 'structure' of a model and some measurements of the time series of the variables of interest (concentrations and fluxes), but little or no knowledge of the model's parameters. This is, then, a system identification problem, that is commonly addressed by running a model with estimated parameters and assessing how far the model's behaviour is from the 'target' behaviour of the variables, and adjusting parameters iteratively until a good fit is achieved. The issue is that most of these problems are grossly underdetermined, such that many combinations of parameters can be used to fit a given set of variables. We introduce the constraint that the estimated parameters should be within given bounds and as close as possible to stated nominal values. This deterministic 'proximate parameter tuning' algorithm turns out to be exceptionally effective, and we illustrate its utility for models of p38 signalling, of yeast glycolysis and for a benchmark dataset describing the thermal isomerisation of α-pinene. This journal is © The Royal Society of Chemistry.
    Original languageEnglish
    Pages (from-to)74-97
    Number of pages23
    JournalMolecular BioSystems
    Volume4
    Issue number1
    DOIs
    Publication statusPublished - 2007

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