At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available –omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of –omics data into CBMs focussing on the methods’ assumptions and limitations. We argue that key assumptions – often derived from single-enzyme kinetics – do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for –omics data integration in a common framework to provide more accurate predictions.
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October 2018
Issue Editors
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Cover Image
Cover Image
This issue of Essays in Biochemistry provides an overview of current research at the interface of the disciplines of biochemistry and systems biology and also looks ahead to future interactions. The cover image, based on Figure 2 in the systems biology primer article by Tavassoly et al., depicts the current computational methods used to analyze different types of high-throughput as well as small scale in-depth experimental data in systems biology. For further details, see pages 487-500.
Review Article|
October 12 2018
Integrating –omics data into genome-scale metabolic network models: principles and challenges
Charlotte Ramon;
Charlotte Ramon
*
1Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Basel 4058, Switzerland
2PhD Program Systems Biology, Life Science Zurich Graduate School, Zurich, Switzerland
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Mattia G. Gollub;
Mattia G. Gollub
*
1Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Basel 4058, Switzerland
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Jörg Stelling
1Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Basel 4058, Switzerland
Correspondence: Jörg Stelling (joerg.stelling@bsse.ethz.ch)
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Publisher: Portland Press Ltd
Received:
April 20 2018
Revision Received:
August 30 2018
Accepted:
August 31 2018
Online ISSN: 1744-1358
Print ISSN: 0071-1365
© 2018 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society
2018
Essays Biochem (2018) 62 (4): 563–574.
Article history
Received:
April 20 2018
Revision Received:
August 30 2018
Accepted:
August 31 2018
Citation
Charlotte Ramon, Mattia G. Gollub, Jörg Stelling; Integrating –omics data into genome-scale metabolic network models: principles and challenges. Essays Biochem 26 October 2018; 62 (4): 563–574. doi: https://doi.org/10.1042/EBC20180011
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