The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Metabolite concentrations, fluxes and free energies imply efficient enzyme usage
Open accessAbstract
In metabolism, available free energy is limited and must be divided across pathway steps to maintain a negative ΔG throughout. For each reaction, ΔG is log proportional both to a concentration ratio (reaction quotient to equilibrium constant) and to a flux ratio (backward to forward flux). Here we use isotope labeling to measure absolute metabolite concentrations and fluxes in Escherichia coli, yeast and a mammalian cell line. We then integrate this information to obtain a unified set of concentrations and ΔG for each organism. In glycolysis, we find that free energy is partitioned so as to mitigate unproductive backward fluxes associated with ΔG near zero. Across metabolism, we observe that absolute metabolite concentrations and ΔG are substantially conserved and that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site dissociation constant (Km or Ki). The observed conservation of metabolite concentrations is consistent with an evolutionary drive to utilize enzymes efficiently given thermodynamic and osmotic constraints.
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“In general, average metabolite concentrations generated from the model (Figure 6) show reasonable agreement to the values reported by Park et al. (2016) in E. coli, but the cell-to-cell variation in the kinetic models over a cell cycle is broader than the predicted range.”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Park et al. (2016)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The main deviation here is again with the FBA reaction where the kinetic parameters result in a free energy drop of roughly 25 kJ/mol which has been reported to be less than 10 kJ/mol (Flamholz et al., 2013; Park et al., 2016).”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Park et al., 2016
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“Other studies have reported overall free energy changes from −30 to −70 kJ/mol (Flamholz et al., 2013; Park et al., 2016)”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Park et al., 2016
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“At the moment, only relative metabolomics data on Syn3A is available, so the metabolite concentrations used to initialize the simulations of the Syn3A WCM were estimated from a scaling of the comprehensive study done on E. coli (Park et al., 2016) and a limited list from M. pneumoniae (Yus et al., 2009).”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Park et al., 2016
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“While such high concentrations have been reported in yeast (Park et al., 2016), they may be inaccurate for Syn3A.”
From Fundamental behaviors emerge from simulations of a living minimal cell (2022) · cites this paper as Park et al., 2016
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Citations
Cites
Data, information, knowledge and principle: back to metabolism in KEGG
Kanehisa, M. I., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1076 · Cited by 3,103
Protein kinases — the major drug targets of the twenty-first century?
Cohen, P. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd773 · Cited by 2,232 · cited 1×
“Journal Nature chemical biology, 12(7)”
Fundamentals of Enzyme Kinetics
Cornish-Bowden, A. J. (1979) · Elsevier eBooks · doi:10.1016/c2013-0-04130-8 · Cited by 2,079
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Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli
Bennett, B. D., Kimball, E. H., Gao, M., Osterhout, R., Van Dien, S. J., & Rabinowitz, J. D. (2009) · Nature Chemical Biology · doi:10.1038/nchembio.186 · Cited by 1,830 · cited 3×
“To understand how cells manage this challenge, biochemists have long relied on the ability to infer the free energies of cellular reactions (ΔG) based on metabolite concentrations combined with standard free energies27,28 as per equation (1).”
BackgroundMethodFeist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Vassily Hatzimanikatis, A., & Palsson, B. Ø. (2007) · Molecular Systems Biology · doi:10.1038/msb4100155 · Cited by 1,408
Understanding the control of metabolism
(1997) · Choice Reviews Online · doi:10.5860/choice.34-6252 · Cited by 1,230
The Regulation of Cellular Systems
Heinrich, R., & Schuster, S. (1996) · Springer eBooks · doi:10.1007/978-1-4613-1161-4 · Cited by 1,202
A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations
Raamsdonk, L. M., Teusink, B., Broadhurst, D., Zhang, N., Hayes, A., Walsh, M. C., BERDEN, J. A., Brindle, K. M., Kell, D. B., Rowland, J. J., et al. (2001) · Nature Biotechnology · doi:10.1038/83496 · Cited by 1,021
Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast
de Godoy, L. M. F., Olsen, J. V., Cox, J., Nielsen, M. L., Hubner, N. C., Fröhlich, F., Walther, T. C., & Mann, M. (2008) · Nature · doi:10.1038/nature07341 · Cited by 873
Cited by
Sorted by citations
Mitochondrial TCA cycle metabolites control physiology and disease
Martínez-Reyes, I., & Chandel, N. S. (2020) · Nature Communications · doi:10.1038/s41467-019-13668-3 · Cited by 2,813
Role of Glutathione in Cancer: From Mechanisms to Therapies
Kennedy, L., Sandhu, J. K., Harper, M.-E., & Cuperlovic-Culf, M. (2020) · Biomolecules · doi:10.3390/biom10101429 · Cited by 880
“Additionally, GSH is not only the most prominent non-protein thiol in cells, it is also one of the most abundant metabolites, which further illustrates the demand for transport [66].”
BackgroundMetabolomics and Isotope Tracing
Jang, C., Chen, L., & Rabinowitz, J. D. (2018) · Cell · doi:10.1016/j.cell.2018.03.055 · Cited by 841
“, by growing cells in [U-(13)C]-glucose) (Bennett et al., 2008; Neubauer et al., 2012; Park et al., 2016).”
Glutamine Metabolism in Cancer: Understanding the Heterogeneity
Cluntun, A. A., Lukey, M. J., Cerione, R. A., & Locasale, J. W. (2017) · Trends in Cancer · doi:10.1016/j.trecan.2017.01.005 · Cited by 796
Fast automated reconstruction of genome-scale metabolic models for microbial species and communities
Machado, D., Andrejev, S., Tramontano, M., & Patil, K. R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky537 · Cited by 754
“A recent study showed that absolute metabolite concentrations are conserved among different kingdoms of life (32).”
Background
Metadata
- Authors
- Park, J. O., Rubin, S. A., Xu, Y.-F., Amador-Noguez, D., Fan, J., Shlomi, T., & Rabinowitz, J. D.
- Affiliations
- Princeton University (US)
- Technion – Israel Institute of Technology (IL)
- Corresponding author
- Rabinowitz, Joshua D
- Venue
- Nature Chemical Biology
- Published
- 2016-05-02
- Volume
- 12
- Issue
- 7
- Pages
- 482-489
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC4912430
- OpenAlex
- W2346094579
- Cited by
- 453
- References
- 60
- Citations by year
- 2026: 39 · 2025: 56 · 2024: 53 · 2023: 50 · 2022: 44 · 2021: 43 · 2020: 40 · 2019: 37 · 2018: 43 · 2017: 34 · 2016: 13
- FWCI
- 22.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Chemical Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- U.S. Department of Energy (DESC0012461)
- National Institutes of Health
- National Cancer Institute (R01CA163591)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Molecular Research
- Keywords
- metabolic flux, metabolite concentrations, enzyme catalytic efficiency, isotopic labeling, glycolysis, reaction quotient, equilibrium constants, Escherichia coli, yeast, thermodynamics of metabolism
- MeSH terms
- Animals, Cell Line, Enzymes, Escherichia coli, Saccharomyces cerevisiae, Thermodynamics, Mice
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