Diverse metabolic model parameters generate similar methionine cycle dynamics

Matthew Piazza, Xiao Jiang Feng, Joshua D. Rabinowitz, Herschel Rabitz

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Parameter estimation constitutes a major challenge in dynamic modeling of metabolic networks. Here we examine, via computational simulations, the influence of system nonlinearity and the nature of available data on the distribution and predictive capability of identified model parameters. Simulated methionine cycle metabolite concentration data (both with and without corresponding flux data) was inverted to identify model parameters consistent with it. Thousands of diverse parameter families were found to be consistent with the data to within moderate error, with most of the parameter values spanning over 1000-fold ranges irrespective of whether flux data was included. Due to strong correlations within the extracted parameter families, model predictions were generally reliable despite the broad ranges found for individual parameters. Inclusion of flux data, by strengthening these correlations, resulted in substantially more reliable flux predictions. These findings suggest that, despite the difficulty of extracting biochemically accurate model parameters from system level data, such data may nevertheless prove adequate for driving the development of predictive dynamic metabolic models.

Original languageEnglish (US)
Pages (from-to)628-639
Number of pages12
JournalJournal of Theoretical Biology
Issue number4
StatePublished - Apr 21 2008

All Science Journal Classification (ASJC) codes

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability
  • Modeling and Simulation


  • Biochemical modeling
  • Cyclic pathway
  • Data inversion
  • Metabolism
  • Parameter identification


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