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Dynamic models for metabolomics data integration.

Curr. Opin. Syst. Biol. 28:100358 (2021)
Publ. Version/Full Text DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and although challenges remain, dynamic modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential.
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Publication type Article: Journal article
Document type Review
Keywords Data Integration ; Dynamic Model ; Kinetic Modeling ; Mechanistic Modeling ; Metabolic Modeling ; Metabolomics
ISSN (print) / ISBN 2452-3100
e-ISSN 2452-3100
Quellenangaben Volume: 28, Issue: , Pages: , Article Number: 100358 Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam
Reviewing status Peer reviewed