ArticleJournal of theoretical biology2008
Diverse metabolic model parameters generate similar methionine cycle dynamics.
Article in Journal of theoretical biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- System drift and speciation.Evolution; international journal of organic evolution · 2022Article
- A novel mathematical model of ATM/p53/NF- κB pathways points to the importance of the DDR switch-off mechanisms.BMC systems biology · 2016Article
- A Bayesian active learning strategy for sequential experimental design in systems biology.BMC systems biology · 2014Article
- Structural identifiability of systems biology models: a critical comparison of methods.PloS one · 2011Article
- An iterative identification procedure for dynamic modeling of biochemical networks.BMC systems biology · 2010Article
- The logic of the hepatic methionine metabolic cycle.Biochimica et biophysica acta · 2010Review
- Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli.Nature chemical biology · 2009Article
- Identification of neutral biochemical network models from time series data.BMC systems biology · 2009Article
- Metabolomics-driven quantitative analysis of ammonia assimilation in E. coli.Molecular systems biology · 2009Article
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4 authors.
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Abstract
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.
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