Metabolic network segmentation: a probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data
Open accessAbstract
In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.
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Metabolic flexibility modeling can give insight into the evolution of metabolic pathways, demonstrating that genes encoding enzymes with high connectivity and high metabolic flux have a greater chance of conservation and are the most likely sites of metabolic regulation.
“Finally, metabolic flexibility modeling can give us insight into the evolution of metabolic pathways (291), demonstrating that genes encoding enzymes with high connectivity and high metabolic flux have a greater chance of conservation (12) and are the most likely sites of metabolic regulation (292).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [292]
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Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease
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Self-citation
- Authors
- Kuehne, A., Mayr, U., Sévin, D. C., Claassen, M., & Zamboni, N.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Claassen, Manfred
- Zamboni, Nicola
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-06-09
- Volume
- 13
- Issue
- 6
- Pages
- e1005577
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5482507
- OpenAlex
- W2623369714
- Cited by
- 9
- References
- 62
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 2 · 2019: 2 · 2018: 2 · 2017: 1
- FWCI
- 0.56
- Type
- journal article
- Language
- en
- Update notices
- new_version (2017-06-23)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- SystemsX.ch
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- metabolic regulation, probabilistic graphical models, untargeted metabolomics, metabolic modules, genome-scale metabolic networks, bacterial metabolism
- MeSH terms
- Computer Graphics, Computer Simulation, Gene Expression Regulation, Models, Biological, Models, Statistical, Proteome, Gene Expression Profiling, Metabolic Networks and Pathways, Metabolomics, Metabolome, Metabolic Flux Analysis
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