#6245. Dynamic models for metabolomics data integration

September 2026publication date
Proposal available till 18-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Applied Mathematics;
Modeling and Simulation;
Computer Science Applications;
Biochemistry, Genetics and Molecular Biology (all);
Drug Discovery;
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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. Dynamic models of metabolism allow for the integration of heterogeneous data and the analysis of dynamic phenotypes. Here, we review recent efforts in using dynamic metabolic models for data integration, focusing on approaches based on ordinary differential equations that are applicable to both time-resolved and steady-state measurements and that do not require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges.
Keywords:
Data integration; Dynamic model; Kinetic modeling; Mechanistic modeling; Metabolic modeling; Metabolomics

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