ArticleMetabolites2012
Ensemble kinetic modeling of metabolic networks from dynamic metabolic profiles.
Article in Metabolites, 2012. 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.
- Improving dynamic predictions with ensembles of observable models.Bioinformatics (Oxford, England) · 2023Article
- Publishing reproducible dynamic kinetic models.Briefings in bioinformatics · 2021Article
- Predicting network modules of cell cycle regulators using relative protein abundance statistics.BMC systems biology · 2017Article
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.PLoS computational biology · 2017Article
- Effective Estimation of Dynamic Metabolic Fluxes Using (13)C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability.Metabolites · 2015Article
- A Kinetic Platform to Determine the Fate of Hydrogen Peroxide in Escherichia coli.PLoS computational biology · 2015Article
- REDEMPTION: reduced dimension ensemble modeling and parameter estimation.Bioinformatics (Oxford, England) · 2015Article
- Mathematical modelling of metabolic regulation in aging.Metabolites · 2015Review
- Computational strategies for a system-level understanding of metabolism.Metabolites · 2014Review
- Article
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Authors and funding
3 authors.
Funding
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Abstract
Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional "best-fit" models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.