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- W3163996845 abstract "As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamical models of metabolism allow for the integration of heterogeneous data and the analysis of dynamical phenotypes. Here, we review recent efforts in using dynamical metabolic models for data integration, focusing on approaches that are not restricted to steady-state measurements or that require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and that, although challenges remain, dynamical modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential." @default.
- W3163996845 created "2021-06-07" @default.
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- W3163996845 date "2021-05-21" @default.
- W3163996845 modified "2023-09-27" @default.
- W3163996845 title "Dynamical models for metabolomics data integration" @default.
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