ArticleFrontiers in genetics2016
Identification of Metabolic Pathway Systems.
Article in Frontiers in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems.Complexity · 2020Article
- Understanding biochemical design principles with ensembles of canonical non-linear models.PloS one · 2020Article
- Mathematical models of lignin biosynthesis.Biotechnology for biofuels · 2018Article
- 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
- Perspective: On the importance of hydrodynamic interactions in the subcellular dynamics of macromolecules.The Journal of chemical physics · 2016Article
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Authors and funding
2 authors.
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Abstract
The estimation of parameters in even moderately large biological systems is a significant challenge. This challenge is greatly exacerbated if the mathematical formats of appropriate process descriptions are unknown. To address this challenge, the method of dynamic flux estimation (DFE) was proposed for the analysis of metabolic time series data. Under ideal conditions, the first phase of DFE yields numerical representations of all fluxes within a metabolic pathway system, either as values at each time point or as plots against their substrates and modulators. However, this numerical result does not reveal the mathematical format of each flux. Thus, the second phase of DFE selects functional formats that are consistent with the numerical trends obtained from the first phase. While greatly facilitating metabolic data analysis, DFE is only directly applicable if the pathway system contains as many dependent variables as fluxes. Because most actual systems contain more fluxes than metabolite pools, this requirement is seldom satisfied. Auxiliary methods have been proposed to alleviate this issue, but they are not general. Here we propose strategies that extend DFE toward general, slightly underdetermined pathway systems.
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