ArticleBioinformatics (Oxford, England)2011
Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment.Cancers · 2021Review
- Perspective: Dimensions of the scientific method.PLoS computational biology · 2019Article
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
- The best models of metabolism.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- Identification of Metabolic Pathway Systems.Frontiers in genetics · 2016Article
- Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data.Frontiers in molecular biosciences · 2016Review
- A unified framework for estimating parameters of kinetic biological models.BMC bioinformatics · 2015Article
- Parameter estimation of dynamic biological network models using integrated fluxes.BMC systems biology · 2014Article
- Derivative processes for modelling metabolic fluxes.Bioinformatics (Oxford, England) · 2014Article
- PENDISC: a simple method for constructing a mathematical model from time-series data of metabolite concentrations.Bulletin of mathematical biology · 2014Article
- MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.BMC bioinformatics · 2014Article
- Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling.Journal of the American Statistical Association · 2014Article
- A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network.BMC systems biology · 2014Article
- Characterizability of metabolic pathway systems from time series data.Mathematical biosciences · 2013Article
- SPEDRE: a web server for estimating rate parameters for cell signaling dynamics in data-rich environments.Nucleic acids research · 2013Article
- A framework for scalable parameter estimation of gene circuit models using structural information.Bioinformatics (Oxford, England) · 2013Article
- Systematic parameter estimation in data-rich environments for cell signalling dynamics.Bioinformatics (Oxford, England) · 2013Article
- Incremental parameter estimation of kinetic metabolic network models.BMC systems biology · 2012Article
- Systems solutions by lactic acid bacteria: from paradigms to practice.Microbial cell factories · 2011Review
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3 authors.
Funding
Abstract
motivationTime-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints.
resultsIn this work, an incremental and iterative parameter estimation method is proposed that combines and iterates between two estimation phases. One phase involves a decoupling method, in which a subset of model parameters that are associated with measured metabolites, are estimated using the minimization of slope errors. Another phase follows, in which the ODE model is solved one equation at a time and the remaining model parameters are obtained by minimizing concentration errors. The performance of this two-phase method was tested on a generic branched metabolic pathway and the glycolytic pathway of Lactococcus lactis. The results showed that the method is efficient in getting accurate parameter estimates, even when some information is missing.
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