ArticleBioinformatics (Oxford, England)2008
System estimation from metabolic time-series data.
Article in Bioinformatics (Oxford, England), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Understanding Iron and Oxidative Stress Response inbioRxiv : the preprint server for biology · 2026Article
- Optimal control of agent-based models via surrogate modeling.PLoS computational biology · 2025Article
- Article
- Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data.Molecular omics · 2023Article
- Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework.Scientific reports · 2022Article
- Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production inMetabolites · 2020Article
- Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems.Complexity · 2020Article
- LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism.BMC bioinformatics · 2020Article
- PARAMETER AND UNCERTAINTY ESTIMATION FOR DYNAMICAL SYSTEMS USING SURROGATE STOCHASTIC PROCESSES.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2019Article
- Application of one-step method to parameter estimation in ODE models.Statistica Neerlandica · 2018Article
- The three-legged stool of understanding metabolism: integrating metabolomics with biochemical genetics and computational modeling.AIMS microbiology · 2018Article
- Identification of microbiota dynamics using robust parameter estimation methods.Mathematical biosciences · 2017Article
- The best models of metabolism.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- Stepwise inference of likely dynamic flux distributions from metabolic time series data.Bioinformatics (Oxford, England) · 2017Article
- Nonparametric dynamic modeling.Mathematical biosciences · 2017Article
- Effects of Storage Time on Glycolysis in Donated Human Blood Units.Metabolites · 2017Article
- Identification of Metabolic Pathway Systems.Frontiers in genetics · 2016Article
- Effective Estimation of Dynamic Metabolic Fluxes Using (13)C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability.Metabolites · 2015Article
- Design Principles as a Guide for Constraint Based and Dynamic Modeling: Towards an Integrative Workflow.Metabolites · 2015Article
- Dynamics of the Heat Stress Response of Ceramides with Different Fatty-Acyl Chain Lengths in Baker's Yeast.PLoS computational biology · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationAt the center of computational systems biology are mathematical models that capture the dynamics of biological systems and offer novel insights. The bottleneck in the construction of these models is presently the identification of model parameters that make the model consistent with observed data. Dynamic flux estimation (DFE) is a novel methodological framework for estimating parameters for models of metabolic systems from time-series data. DFE consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model-based mathematical characterization of process representations. The model-free phase reveals inconsistencies within the data, and between data and the alleged system topology, while the model-based phase allows quantitative diagnostics of whether--or to what degree--the assumed mathematical formulations are appropriate or in need of improvement. Hallmarks of DFE are the facility to: diagnose data and model consistency; circumvent undue compensation of errors; determine functional representations of fluxes uncontaminated by errors in other fluxes and pinpoint sources of remaining errors. Our results suggest that the proposed approach is more effective and robust than presently available methods for deriving metabolic models from time-series data. Its avoidance of error compensation among process descriptions promises significantly improved extrapolability toward new data or experimental conditions.
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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.