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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)”
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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“Based on the FBP aldolase reaction mechanism, which generates comparable fractions of M + 0 and M + 4 FBP in the reverse reaction 63 , FBP M + 0 and M + 4 fractions were set equal to each other in the [1,2-13 C 2 ]glucose experiments for 13 C-based metabolic flux analysis.”
InfluentialHe, H., Gómez-Coronado, P. A., Zarzycki, J., Barthel, S., Kahnt, J., Claus, P., Klein, M., Klose, M., de Crécy‐Lagard, V., Schindler, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53156-x · Cited by 19
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Breakdown and repair of metabolism in the aging brain
Shichkova, P., Coggan, J. S., Kanari, L., Boci, E., Favreau, C., Antonel, S., Keller, D. X., & Markram, H. (2025) · Frontiers in Science · doi:10.3389/fsci.2025.1441297 · Cited by 22
Felczak, M. M., Jacobson, T. B., Ong, W. K., Amador‐Noguez, D., & TerAvest, M. A. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02270 · Cited by 19
“In E. coli, this ratio is ~0.1, allowing the DHAP to GAP conversion to operate close to equilibrium (ΔrG′ = −0.2 ± 1.1 kJ/mol) (Park et al., 2016).”
InfluentialMolecular determinants of phase separation for Drosophila DNA replication licensing factors
Parker, M. W., Kao, J., Huang, A., Berger, J. M., & Botchan, M. R. (2021) · eLife · doi:10.7554/elife.70535 · Cited by 17
“…ability of Cy5-dsDNA to induce phase separation over a range of protein (40–5000 nM) and KGlutamate (50–300 mM) concentrations that span physiological conditions (in vivo, [Cdt1] ≈ 100 nM [ Parker et al., 2019 ] and [salt] ≈ 150 mM [ Kretsinger et al., 2013 ; Park et al., 2016 ]; Figure 2F, G ).”
Dissecting Flux Balances to Measure Energetic Costs in Cell Biology: Techniques and Challenges
Arunachalam, E., Ireland, W. T., Yang, X., & Needleman, D. J. (2022) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-031620-105251 · Cited by 18
“Other ATPases can have a Michaelis-Menten constant KM close to intracellular ATP/ADP levels, and their reaction rates and directions could be similarly sensitive to changes in ATP/ADP induced by perturbations [8, 12].”
BackgroundRational strain design with minimal phenotype perturbation
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Dynamics in redox metabolism, from stoichiometry towards kinetics
Verhagen, K. J. A., van Gulik, W. M., & Wahl, A. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.01.002 · Cited by 18
“Compartmentation allows the establishment of different environments within a cell, thereby providing a wider range of thermodynamic possibilities [8].”
BackgroundControl of topoisomerase II activity and chemotherapeutic inhibition by TCA cycle metabolites
Lee, J. H., Mosher, E. P., Lee, Y., Bumpus, N. N., & Berger, J. M. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.08.014 · Cited by 18
Pokhrel, A., Poudel, A., Castro, K. B., Celestine, M. J., Oludiran, A., Rinehold, A. J., Resek, A. M., Mhanna, M. A., & Purcell, E. B. (2020) · Journal of Bacteriology · doi:10.1128/jb.00377-20 · Cited by 16
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Engineering metabolism to modulate immunity
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Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution
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Evaluation of kinetic data: What the numbers tell us about PRMTs
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Estimating Metabolic Equilibrium Constants: Progress and Future Challenges
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Marszałek, I., Goch, W., & Bal, W. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b02419 · Cited by 16
Queueing theory model of Krebs cycle
Kloska, S. M., Pałczyński, K., Marciniak, T., Talaśka, T., Nitz, M., Wysocki, B. J., Davis, P. H., & Wysocki, T. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab177 · Cited by 16
Enhanced 13 C NMR detects extended reaction networks in living cells
Jensen, P. R., Sannelli, F., Stauning, L. T., & Meier, S. (2021) · Chemical Communications · doi:10.1039/d1cc03838a · Cited by 16
Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes
Law, R. C., Lakhani, A., O’Keeffe, S., Erşan, S., & Park, J. O. (2022) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102701 · Cited by 16
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Adamczyk, P., Hwang, H. J., Chang, T.-H., Gao, Y., Baidoo, E. E. K., Kim, J., Webb‐Robertson, B., Flores, J. E., Quijano, K. C., Burnet, M., et al. (2025) · Metabolic Engineering · doi:10.1016/j.ymben.2025.02.014 · Cited by 15
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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