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Parameter Estimation, Metabolic Network Modeling

Abstract:

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) optimization procedures are frequently used estimation approaches. Experimental data usually contain a time-course or steady state of concentration values of the reacting species (compounds) within the network. Before estimating their values, a parameter identifiability analysis should be conducted. Estimated values should be analyzed regarding their thermodynamic plausibility.

10.1007/978-1-4419-9863-7_1174

Projects: A3.4: Linking signalling to metabolic functions, B5: Cell-cell communication influences detoxifying functions in hepatocytes

Encyclopedia of Systems Biology
Encyclopedia of Systems Biology : 1627
2013

Andreas Dräger, Hannes Planatscher

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[Andreas Dräger]

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  • Created: 15th Aug 2013 at 23:37
  • Last updated: 23rd Jul 2015 at 13:38

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