Reference Work Entry

Encyclopedia of Systems Biology

pp 1627-1631

Parameter Estimation, Metabolic Network Modeling

  • Andreas DrägerAffiliated withCenter for Bioinformatics, University of Tuebingen Email author 
  • , Hannes PlanatscherAffiliated withNatural and Medical Sciences Institute at the University of Tuebingen


Model calibration; Model fitting; Parameter fitting; Parameter identification; Regression


Metabolic networks can mathematically be modeled as a differential equation system. These models consist of a stoichiometric matrix, a modulation matrix, and a vector of reaction rates. The mathematical structure of the differential equation system is usually assumed to be known, its parameters, however, in general are not. Here the term “parameter” denotes all quantities within the model whose values are uncertain or difficult to obtain experimentally. In the most common case, these comprise kinetic constants, concentrations of external metabolites, enzyme concentrations or activities, and compartment sizes. The parameter estimation problem aims at estimating meaningful values for these targets by trying to coincide given experimental data with the predictions of the model. Bayesian methods, maximum likelihood estimates, and (biologically inspired)

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