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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)
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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)”
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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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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
Physiological concentrations of purines and pyrimidines
Traut, T. W. (1994) · Molecular and Cellular Biochemistry · doi:10.1007/bf00928361 · Cited by 1,867
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
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Items 401–453 of 453
Nielsen, J. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.731119252.793550639
A parallel glycolysis supports rapid adaptation in dynamic environmentsPreprint
Law, R. C., Nurwono, G., & Park, J. O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.19.504590
An engineered biosensor enables dynamic aspartate measurements in living cellsPreprint
Davidsen, K., Marvin, J. S., Aggarwal, A., Brown, T. A., & Sullivan, L. B. (2023) · eLife · doi:10.7554/elife.90024.1
Energy optimisation predicts the capacity of ion buffering in the brain
Maex, R. (2023) · Biological Cybernetics · doi:10.1007/s00422-023-00980-x
“…denoted by A − , do not permeate the membrane (Armstrong 2015); they represent (osmotically active) compounds such as charged amino acids (glutamate and aspartate), phosphorylated metabolites, and macromolecules, all with an average valency of − 1 (Westheimer 1987; Jäckle 2007; Park et al. 2016).”
A method to identify high consensus predictions of single-cell metabolic fluxPreprint
Amiss, M., Lum, J. J., & Jabbari, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.15.572211
“Metabolism can be also be assessed by measuring metabolic flux [5,20].”
BackgroundResultInfluentialAn engineered biosensor enables dynamic aspartate measurements in living cellsPreprint
Davidsen, K., Marvin, J. S., Aggarwal, A., Brown, T. A., & Sullivan, L. B. (2024) · eLife · doi:10.7554/elife.90024.2
Balcke, G. U., Saoud, M., Grau, J., Rennert, R., Mueller, T., Yousefi, M., Davari, M. D., Hause, B., Csük, R., Rashan, L. J., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3494185/v1
Overflow metabolism originates from growth optimization and cell heterogeneityPreprint
Wang, X. (2024) · eLife · doi:10.7554/elife.94586.1
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Tremblay, A., Bekiaris, P. S., Klamt, S., & Mahadevan, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.19.585265
“The minimal and maximal standard metabolite concentrations were bounded according to physiological data [43,44].”
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Che, Y.-F., Li, R., & Man, Q. (2024) · Journal of Stomatology Oral and Maxillofacial Surgery · doi:10.1016/j.jormas.2024.101873
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Maitra, A., Lim, J. J. H., Ho, C. J. Y., Tang, A. Y., Teo, W., Alejado, E. L. C., & Ling, M. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00069-5
Plazolles, N., Kulyk, H., Cahoreau, E., Biran, M., Wargnies, M., Pineda, E., Kadri, M. E., Rimoldi, A., Hervé, P., Asencio, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.14.623581
Guo, X., Wang, X., Hu, Y., Zhang, L., & Zhao, Z. K. (2024) · Scientific Reports · doi:10.1038/s41598-024-79885-z
Overflow metabolism originates from growth optimization and cell heterogeneityPreprint
Wang, X. (2024) · eLife · doi:10.7554/elife.94586.2
Boada, Y., Flores, M., Stiebritz, M. T., Córdova, M., Flores, F. J., & Vignoni, A. (2025) · Synthetic Biology · doi:10.1093/synbio/ysaf006
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Kiel, C., & Foss, A. J. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-06356-4 · Cited by 1
“Intracellular AA concentrations for RPE and rPR cells were estimated from measurements in mammalian iBMK cells 63 (Supplementary Table 4—sheet 2).”
Sun, K., Zheng, W., Fan, W., Ding, C., Huang, D., & Dai, Z. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.29.679393
“Experimentally measured absolute metabolite concentrations 27, 38, 46, 47 in human cells, the bacteria Escherichia coli , and the unicellular eukaryote Saccharomyces cerevisiae were used together with the enzymatic parameters to estimate the catalytic efficiency .”
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Yuan, B., Wei, R., Brier, T. A., Fu, E., Thornburg, Z. R., Luthey-Schulten, Z. A., & Wang, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.30.685684
“…characterization of reaction kinetics, whole-cell models computationally track the temporal evolution of thousands of molecular species and intermediates across genomic, transcriptomic, proteomic, and metabolomic layers (Bremer and Dennis, 2008; Taniguchi et al., 2010; Park et al., 2016).”
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Ahn‐Jarvis, J. H., Defernez, M., Gall, G. L., & Paxhia, M. D. (2025) · Springer protocols handbooks/Springer protocols · doi:10.1007/978-1-0716-5009-7_13
Thomas, A. M., Yang, E., Smith, M. D., Chu, C., Calabresi, P. A., Glunde, K., van Zijl, P. C. M., & Bulte, J. W. M. (2022) · Figshare · doi:10.6084/m9.figshare.20000087
Thomas, A. M., Yang, E., Smith, M. D., Chu, C., Calabresi, P. A., Glunde, K., van Zijl, P. C. M., & Bulte, J. W. M. (2022) · Figshare · doi:10.6084/m9.figshare.20000084
Thomas, A. M., Yang, E., Smith, M. D., Chu, C., Calabresi, P. A., Glunde, K., van Zijl, P. C. M., & Bulte, J. W. M. (2022) · Figshare · doi:10.6084/m9.figshare.20000090
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“Similarly, in examining the TCA cycle derivatives (Figure 5E), both citrate and cis-Aconitate were able to inhibit PFKFB3 within physiological ranges (5 mM and 1 mM, respectively) 40 .”
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Xiao, T. L., Zhao, W., Yang, R., & Lyu, X. (2026) · Biochemical Engineering Journal · doi:10.1016/j.bej.2026.110158
Julien-Laferrière, A. (2016) · theses.fr (ABES) · doi:10.70675/15b4c850zcf4ez45e2z96c2z73e419c6e6cd
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Witus, S. R., Kober, M. M., Roh, H., Yang, Z., Choueiry, F., Ghate, A. S., Titov, D. V., & Rapé, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.05.723063
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Sender, R., Kedar, T., Navon, Y., Raz, M., Bikel, S., Hemi, R., Milo, R., & Fuchs, S. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003864
“These patterns are compatible with concentration-affinity matching principles analogous to those in metabolic networks [22], where substrate concentrations are maintained high enough to keep enzyme binding sites occupied.”
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Guo, J., Wang, J., Hong, Y., & Xu, P. (2026) · Trends in biotechnology · doi:10.1016/j.tibtech.2026.06.005
Yang, X., & Needleman, D. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.28.735091
“Kinetic rates relevant to metabolic pathways are difficult to measure, but metabolic fluxes, which usually depend on a combination of these kinetic rates and metabolite concentrations, can be measured using a variety of different approaches 3 .”
Berndsen, C., Kayll, A. J., Rahman, R., Scanlan, S. E., Tester, J. R., Beaver, T., Caroline, T., Neve-Hoversten, S., Mandanis, T., Gentile, L., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7302430
Selective Redox Tuning Enables Potent Intracellular Reduction of Nicotinamide Cytosine Dinucleotide
Guo, X., Huang, Y., Hu, Y., Zhang, L., & Zhao, Z. K. (2026) · Biomolecules · doi:10.3390/biom16091255
“For NAD and NCD quantification, the concentration gradient of the standard curve is set to range from 10 to 320 µ M or 160 mM, according to the intracellular concentration of NAD and NCD at the micromolar level [20,28].”
InfluentialProtein-driven colloid-osmotic pressure controls nuclear size, organization, and functionPreprint
Ohsawa, S., Sáncho-Andrés, G., Narduzzi, G., Zhao, W., Lakhe, D., Estrada, M. E., Biswas, A., Moriizumi, H., Zatulovskiy, E. A., Reber, S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.03.749057
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Kocheril, P. A., Leighton, R., Naji, N., Lee, D., & Wei, L. (2026) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6c00380
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Ding, H., Lu, M., Zhang, H., Zheng, P., Zhu, Y., & Sun, J. (2026) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2026.2726280
“They are constrained by reaction driving force (Δg), metabolite availability, enzyme capacity, and the need to preserve flux directionality, ion homeostasis, and stress tolerance [143,144].”
Abekawa, T., Ohno, S., Hirayama, A., Soga, T., & Kuroda, S. (2026) · iScience · doi:10.1016/j.isci.2026.117618
Quinone Reductase 2 Dimerization is Dynamically Driven by Ligand Binding
Miller, M., Loewenthal, D., Kukura, P., & Gould, N. L. (2026) · Biochemistry · doi:10.1021/acs.biochem.6c00461
Guo, X., Wang, Q., Liu, T., Hu, Y., Wang, X., & Zhao, Z. K. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1954596
“Under aerobic growth conditions, the concentration of NADH is lower than that of NAD with an NADH/NAD ratio of as low as 0.03 (Park et al., 2016).”
InfluentialLin, X., Chow, S., Mirzayans, P. M., Tng, J., Schenk, G., Williams, C. M., & Guddat, L. W. (2026) · Biochemistry · doi:10.1021/acs.biochem.6c00555
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Sun, K., Zheng, W., Fan, W., Ding, C., Huang, D., & Dai, Z. (2026) · Molecular Systems Biology · doi:10.1038/s44320-026-00250-5
Hoyt, K. O., Gasparrini, W., Hayes, J. A., & Woolston, B. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.12.758492
Manzanarez, G., Balakrishnan, R., Kannan, H., Abele, M., Okano, H., She, F., Zhang, Z., Wu, C., Freiman, L., Ludwig, C., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2611560123
Rajarajan, T. P., Adam, S., Sangeetha, S., Manian, R. P., Kumar, S. V., & Renganathan, S. (2026) · Green energy and technology · doi:10.1007/978-981-95-9528-0_13
Li, Q., Zhang, J., Su, P., Wan, H., Ping, X., Zhang, C., Dai, R., He, Z., Ni, Y., 赵忠盖, et al. (2026) · Journal of Process Control · doi:10.1016/j.jprocont.2026.103873
Chen, D., Li, L., Gao, Y., Tian, P., Yu, B., Liu, X., & Huang, X. (2026) · ENGINEERING Chemical Engineering · doi:10.1007/s11705-027-2718-2
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Wu, G., Sun, F., Liu, T., Ranjbar, S., Tanıl, E., & Du, B. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.136123
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FAIRsharing Team, McQuilton, P., & Mihail (2018) · FAIRsharing.org · doi:10.25504/fairsharing.7jcjd0
Alekhina, O. M., Bashkirov, P. V., Silantyev, A. S., Dudik, S. P., Matyushkina, D. S., Shcherbakova, O. V., & Govorun, V. M. (2026) · Acta Naturae · doi:10.32607/actanaturae.27965
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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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