Advances in metabolic flux analysis toward genome-scale profiling of higher organisms
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Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.
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FBA has been proven capable of making quantitatively realistic predictions of plant metabolic behavior.
“This approach has been proven capable of making quantitatively realistic predictions of plant metabolic behavior (Basler et al., 2018; Moreira et al., 2019).”
From Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light (2019) · cites this paper as Basler et al., 2018
In “Integration of unlabeled metabolomics data” (page unavailable), the review reports that a constraint-based approach predicted flux differences in Arabidopsis consistent with observed mutant phenotypes. It does not establish that FBA makes quantitatively realistic predictions of plant metabolism: the good agreement between FBA-based predictions and measured fluxes reported in “Highlighted advances of metabolic flux profiling at a genome-scale level” concerns E. coli, and that section cautions against assuming a single objective for plant metabolism. (LLM, conf=0.85)
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Self-citation
- Authors
- Basler, G., Fernie, A. R., & Nikoloski, Z.
- Affiliations
- University of Potsdam (DE)
- Max Planck Institute of Molecular Plant Physiology (DE)
- Corresponding author
- Nikoloski, Zoran
- Venue
- Bioscience Reports
- Publisher
- Portland Press Ltd.
- Published
- 2018-11-23
- Volume
- 38
- Issue
- 6
- ISSN
- 0144-8463 (ISSN-L)
- 1573-4935
- PMC
- PMC6250807
- OpenAlex
- W2896108989
- Cited by
- 41
- References
- 93
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 9 · 2022: 5 · 2021: 11 · 2020: 9 · 2019: 3
- FWCI
- 0.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Bioscience Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- publish.UP (University of Potsdam) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- 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 flux analysis, metabolic labeling, constraint-based modeling, plant metabolism, systems biology
- MeSH terms
- Humans, Genome, Systems Biology, Metabolic Networks and Pathways, Metabolomics, Metabolic Flux Analysis
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