Encyclopedia of Systems Biology

2013 Edition
| Editors: Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota

Parameter Estimation, Metabolic Network Modeling

  • Andreas DrägerEmail author
  • Hannes Planatscher
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-9863-7_1174



 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...

This is a preview of subscription content, log in to check access.


  1. Albert R (2007) Network inference, analysis, and modeling in systems biology. Plant Cell 19(11):3327–3338PubMedGoogle Scholar
  2. Balsa-Canto E, Banga JR (2010) Computational procedures for model identification. In: Choi S (ed) Systems biology for signaling networks, vol 1, Systems biology. Springer, New York, pp 111–137Google Scholar
  3. Klipp E, Liebermeister W, Wierling C, Kowald A, Lehrach H (2009) Systems biology: a textbook, 1st edn. Wiley-VCH, WeinheimGoogle Scholar
  4. Ma H-w, Silva MR, Sun JB, Kumar B, Zeng A-P (2007) Reconstruction and structural analysis of metabolic and regulatory networks. In: Choi S (ed) Introduction to systems biology. Humana Press, Totowa, pp 124–146Google Scholar
  5. Noirel J, Sanguinetti G, Wright PC (2010) Mixture model on graphs: a probabilistic model for network-based analysis of proteomic data. In: Choi S (ed) Systems biology for signaling networks, vol 1, Systems biology. Springer, New York, pp 371–397Google Scholar
  6. Palsson BØ (2006) Systems biology: properties of reconstructed networks, 1st edn. Cambridge University Press, New YorkGoogle Scholar
  7. Sonntag ED (2008) For differential equations with r parameters, 2r + 1 experiments are enough for identification. J Nonlinear Sci 21(6):553–583Google Scholar
  8. Wilkinson DJ (2006) Stochastic modelling for systems biology (Chapman & Hall/CRC Mathematical & Computational Biology), 1st edn. Chapman and Hall/CRC, Boca RatonGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Center for Bioinformatics, University of TuebingenTuebingenGermany
  2. 2.Natural and Medical Sciences Institute at the University of TuebingenReutlingenGermany