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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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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
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 453
NUDT5 regulates purine metabolism and thiopurine sensitivity by interacting with PPAT
Wu, Z., Nguyen, P. T., Sondhi, V., Yao, R.-W., Lu, Z., Dai, T., Chiang, J., Cai, F., Williams, I. M., Blatt, E. B., et al. (2025) · Science · doi:10.1126/science.adx9717 · Cited by 16
ENKIE: a package for predicting enzyme kinetic parameter values and their uncertainties
Gollub, M. G., Backes, T., Kaltenbach, H., & Stelling, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae652 · Cited by 15
“For K M , the substrate is the first grouping level because parameter values for a substrate are conserved across reactions (Park et al. , 2016).”
BackgroundSharma, B. D., Hon, S., Thusoo, E., Stevenson, D. M., Amador‐Noguez, D., Guss, A. M., Lynd, L. R., & Olson, D. G. (2024) · Biotechnology for Biofuels and Bioproducts · doi:10.1186/s13068-024-02591-5 · Cited by 15
“…wild-type C. thermocellum [11] was previously observed to function closer to thermodynamic equilibrium compared to organisms such as Thermoanaerobacterium saccharolyticum or Escherichia coli that utilize a canonical glycolytic pathway [12, 13], and may limit final product titer in C. thermocellum .”
Gawthrop, P. J., Pan, M., & Crampin, E. J. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0478 · Cited by 15
The Metabolome Weakens RNA Thermodynamic Stability and Strengthens RNA Chemical Stability
Sieg, J. P., McKinley, L. N., Huot, M. J., Yennawar, N. H., & Bevilacqua, P. C. (2022) · Biochemistry · doi:10.1021/acs.biochem.2c00488 · Cited by 15
Fernandes, P. M., Kinkead, J., McNae, I. W., Bringaud, F., Michels, P. A. M., & Walkinshaw, M. D. (2018) · Biochemical Journal · doi:10.1042/bcj20180635 · Cited by 15
“coli and a mammalian kidney 335 cell line (25) identified PFK as an almost exclusively forward driven glycolytic step in all cells.”
BackgroundNetwork Thermodynamical Modeling of Bioelectrical Systems: A Bond Graph Approach
Gawthrop, P. J., & Pan, M. (2020) · Bioelectricity · doi:10.1089/bioe.2020.0042 · Cited by 16
The return of metabolism: biochemistry and physiology of glycolysis
Grüning, N., Agostini, F., Caldana, C., Hartl, J., Heinemann, M., Keller, M. A., Krüsemann, J. L., Lamperti, C., Linster, C. L., Lindner, S. N., et al. (2025) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.70104 · Cited by 18
Pathways to Understanding Virus-Host Metabolism Interactions
Purdy, J. G. (2018) · Current Clinical Microbiology Reports · doi:10.1007/s40588-018-0109-7 · Cited by 14
Network thermodynamics of biological systems: A bond graph approach
Gawthrop, P. J., & Pan, M. (2022) · Mathematical Biosciences · doi:10.1016/j.mbs.2022.108899 · Cited by 15
Xiao, Z., Connor, A. J., Worland, A. M., Tang, Y., Zha, R. H., & Koffas, M. A. G. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.03.011 · Cited by 14
Local flux coordination and global gene expression regulation in metabolic modeling
Li, G., Liu, L., Du, W., & Cao, H. (2023) · Nature Communications · doi:10.1038/s41467-023-41392-6 · Cited by 13
Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design
Wei, F., Cai, J., Mao, Y., Wang, R., Li, H., Mao, Z., Liao, X., Li, A., Deng, X., Li, F., et al. (2024) · Advanced Science · doi:10.1002/advs.202404632 · Cited by 13
Dormeyer, M., Lübke, A. L., Müller, P., Lentes, S., Reuß, D. R., Thürmer, A., Stülke, J., Daniel, R., Brantl, S., & Commichau, F. M. (2017) · Environmental Microbiology Reports · doi:10.1111/1758-2229.12531 · Cited by 14
“Glutamate, cell’s most abundant metabolite, fulfils several important functions (Gunka and Commichau, 2012; Park et al., 2016).”
BackgroundVerma, K., Saxena, K., Rajashekar, D., Chaphalkar, A., Kumar, M., Shukla, A., Zaidi, Z., Dandage, R., Shanmugam, D., & Chakraborty, K. (2020) · Nature Communications · doi:10.1038/s41467-020-16804-6 · Cited by 13
Wood, W. N., Mohler, K., Rinehart, J., & Ibba, M. (2021) · mBio · doi:10.1128/mbio.01132-21 · Cited by 14
“coli cells as described previously (55, 56).”
Soma, Y., Takahashi, M., Fujiwara, Y., Tomiyasu, N., Goto, M., Hanai, T., Izumi, Y., & Bamba, T. (2021) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2021.09.009 · Cited by 14
Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions
Jeffryes, J. G., Lerma‐Ortiz, C., Liu, F., Golubev, A. G., Niehaus, T. D., Elbadawi-Sidhu, M., Fiehn, O., Hanson, A. D., Tyo, K. E. J., & Henry, C. S. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.11.009 · Cited by 14
Structural Insights into Phosphite Dehydrogenase Variants Favoring a Non-natural Redox Cofactor
Liu, Y., Feng, Y., Wang, L., Guo, X., Liu, W., Li, Q., Wang, X., Xue, S., & Zhao, Z. K. (2019) · ACS Catalysis · doi:10.2210/pdb6ih8/pdb · Cited by 14
Mitochondria-specific photorelease of ceramide induces apoptosis
Schröer, C., Kol, M. A., Koch, A., Döffinger, E., Annamalai, M., & Holthuis, J. C. M. (2025) · Journal of Lipid Research · doi:10.1016/j.jlr.2025.100907 · Cited by 13
Takaine, M., Morita, R., Yoshinari, Y., & Nishimura, T. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003111 · Cited by 13
“However, millimolar levels of AMP or GMP are required to inhibit PPAT catalytic activity [4,6,7], whereas the intracellular concentrations of these nucleotides are markedly lower [8], raising questions about the physiological relevance of this feedback inhibition.”
Daurio, N. A., Wang, S.-P., Chen, Y., Zhou, H., McLaren, D. G., Roddy, T. P., Johns, D. G., Milot, D. P., Kasumov, T., Erion, M. D., et al. (2017) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.117.241091 · Cited by 13
“A recent study concluded that most intracellular substrate concentrations exceed the Km, implying that metabolic flux more closely approximates zero-order kinetics (Park et al., 2016).”
BackgroundMass Spectrometry in Advancement of Redox Precision Medicine
Chen, X., Lee, J., Wu, H., Tsang, A. W., & Furdui, C. M. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-15950-4_19 · Cited by 12
Berzins, K., Muiznieks, R., Baumanis, M. R., Strazdiņa, I., Shvirksts, K., Prikule, S., Galvanauskas, V., Pleißner, D., Pentjuss, A., Grūbe, M., et al. (2022) · Marine Drugs · doi:10.3390/md20020115 · Cited by 13
“[55], it was implemented as a constraint that the metabolite concentrations in this model should not exceed 12 mmol/L, except for cellular ethanol, which was allowed to reach 32 mmoL/L.”
Protein cost minimization promotes the emergence of coenzyme redundancy
Goldford, J. E., George, A. B., Flamholz, A. I., & Segrè, D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110787119 · Cited by 13
Wordofa, G. G., Kristensen, M., Schrübbers, L. C., McCloskey, D., Förster, J., & Schneider, K. P. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b00793 · Cited by 13
Quantitative Connection between Cell Size and Growth Rate by Phospholipid Metabolism
Zhang, Z., Zhang, Q., Guan, S., & Shi, H. (2020) · Cells · doi:10.3390/cells9020391 · Cited by 14
Glycolysis model shows that allostery maintains high ATP and limits accumulation of intermediates
Choe, M., Einav, T., Phillips, R., & Titov, D. V. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.03.037 · Cited by 13
Baldazzi, V., Ropers, D., Gouzé, J.-L., Gedeon, T., & de Jong, H. (2023) · eLife · doi:10.7554/elife.79815 · Cited by 12
Sugimura, M., Seike, T., Okahashi, N., Izumi, Y., Bamba, T., Ishii, J., & Matsuda, F. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242216378 · Cited by 12
A global view of T cell metabolism in systemic lupus erythematosus
Goetz, A., Cagmat, J. G., Brusko, M., Brusko, T. M., Rushin, A., Merritt, M. E., Garrett, T. J., Morel, L. M., & Dixit, P. D. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1371708 · Cited by 11
Niepmann, M. (2024) · Cancers · doi:10.3390/cancers16132290 · Cited by 12
Biological solution conditions and flanking sequence modulate LLPS of RNA G-quadruplex structures
Williams, A. M., Dickson, T. M., Lagoa-Miguel, C. A., & Bevilacqua, P. C. (2022) · RNA · doi:10.1261/rna.079196.122 · Cited by 11
“For instance, the concentration of ATP is 1.9 mM in yeast and 9.6 mM in E. coli (Supplemental Table 2; Park et al. 2016).”
InfluentialSannelli, F., Jensen, P. R., & Meier, S. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c00225 · Cited by 12
Raven, J. A. (2018) · Journal of Experimental Botany · doi:10.1093/jxb/ery254 · Cited by 13
Information-rich high-throughput cellular assays using acoustic mist ionisation mass spectrometry
Bachman, M., Sinclair, I., Ivanov, D. P., & Wingfield, J. M. (2020) · The Analyst · doi:10.1039/d0an01519a · Cited by 12
Enzymes and Substrates Are Balanced at Minimal Combined Mass Concentration in vivoPreprint
Dourado, H., Maurino, V. G., & Lercher, M. J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/128009 · Cited by 13
“(25) differ substantially: while YNB (26) is a defined medium that contains no”
BackgroundMethodInfluentialPrinciples of metabolome conservation in animals
Liska, O., Boross, G. Z., Rocabert, C., Szappanos, B., Tengölics, R., & Papp, B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2302147120 · Cited by 11
Narayanan, B., Jiang, W., Wang, S., Sáez‐Sáez, J., Weilandt, D., Barcon, M. M., Hesselberg-Thomsen, V., Borodina, I., Vassily Hatzimanikatis, A., & Mišković, L. (2025) · Metabolic Engineering · doi:10.1016/j.ymben.2025.06.008 · Cited by 11
Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18070951 · Cited by 11
Koendjbiharie, J. G., Wevers, K., & van Kranenburg, R. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01162 · Cited by 11
“7 mM for yeast, and mammalian cells (Park et al., 2016).”
BackgroundInfluentialClose, D. M., Cooper, C. J., Wang, X., Chirania, P., Gupta, M., Ossyra, J. R., Giannone, R. J., Engle, N. L., Tschaplinski, T. J., Smith, J. C., et al. (2019) · Molecular Microbiology · doi:10.1111/mmi.14393 · Cited by 11
“The inhibition by 4-hydroxybenzaldehyde under physiological concentrations of IMP (270 μM) and NAD (2500 μM) were determined as well (Bennett et al., 2009; Park et al., 2016).”
BackgroundDormeyer, M., Lentes, S., Richts, B., Heermann, R., Ischebeck, T., & Commichau, F. M. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02321 · Cited by 11
“Keywords: glutamate biosynthesis, glutamate dehydrogenase, trigger enzyme, mutational analysis, promoter INTRODUCTION Glutamate is the most abundant cellular metabolite that serves as an amino group donor in many anabolic reactions (Gunka and Commichau, 2012; Park et al., 2016).”
BackgroundEnzyme activities predicted by metabolite concentrations and solvent capacity in the cell
Britton, S. R., Alber, M., & Cannon, W. R. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2020.0656 · Cited by 11
Font, E. G., & Sprenger, G. A. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21249625 · Cited by 11
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Bartman, C., Shen, Y., Lee, W. D., TeSlaa, T. A., Jankowski, C. S. R., Wang, L., Yang, L., Roichman, A., Bhatt, V. D., Lan, T., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.04.463108 · Cited by 11
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Zhang, J., Li, Z., Pang, Y., Fan, Y., & Ai, H. (2023) · ACS Sensors · doi:10.1021/acssensors.3c00409 · Cited by 10
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Gopich, I. V. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c11155 · Cited by 10
Modi, G. P., Marqus, G. M., Vippila, M. R., Gollapalli, D. R., Kim, Y., Manna, A. C., Chacko, S., Maltseva, N., Wang, X., Cullinane, R. T., et al. (2021) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.1c00342 · Cited by 10
Fadhil, A. E., & Abbas, A. K. (2023) · Iraqi Journal of Science · doi:10.24996/ijs.2023.64.12.4 · Cited by 9
“The stability constant for complexes Ag(I), Zn(II), and Cd(II) at a 1:1 ratio, on the other hand, was calculated using the following equation [6]: According to the findings in Table 3, the stability is as follows, in ascending order: The thermodynamic coefficient of ΔG (Gibbs free energy) was…”
BackgroundMethodInfluentialImproving metabolic engineering design with enzyme-thermo optimization
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Francis, F., Chettri, D., & Nair, D. K. (2025) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2025.1661342 · Cited by 9
Lapashina, A. S., Kashko, N. D., Zubareva, V. M., Galkina, K. V., Маркова, О. В., Кнорре, Д. А., & Feniouk, B. A. (2022) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2022.148544 · Cited by 9
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Stopka, S. A., & Vértes, Á. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0030-6_15 · Cited by 9
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Adhikari, A., Vilkhovoy, M., Vadhin, S., Lim, H. E., & Varner, J. D. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.539081 · Cited by 10
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Guo, X., Liu, Y., Wang, Q., Wang, X., Li, Q., Liu, W., & Zhao, Z. K. (2019) · Angewandte Chemie · doi:10.1002/ange.201915303 · Cited by 9
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Weilandt, D., Masid, M., & Vassily Hatzimanikatis, A. (2021) · Metabolic Engineering · doi:10.1002/9783527823468.ch7 · Cited by 7
Blanc, L. L., Alric, B., Rollin, R., Xénard, L., Finn, L. R., Goussard, S., Mazenq, L., Ingersoll, M. A., Piel, M., Tinévez, J.-Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.20.614086 · Cited by 9
“…metabolites and counterions, which were recently proposed to dominate the intracellular trapped osmolytes, due to their large numbers inside cells ( 43 ); (2) proteins, whose contribution is mostly steric and whose production rate is reported to be sensitive to cytoplasmic crowding( 13 ), but, due…”
BackgroundOverflow metabolism originates from growth optimization and cell heterogeneity
Wang, X. (2024) · eLife · doi:10.7554/elife.94586.4 · Cited by 10
Tian, B., Chen, M., Liu, L., Rui, B., Deng, Z., Zhang, Z., & Shen, T. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.883466 · Cited by 8
Impact of the Cellular Environment on Adenosine Triphosphate Conformations
Rickard, M. M., Luo, H., Lio, A. M. D., Gruebele, M., & Pogorelov, T. V. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c02375 · Cited by 8
Callaghan, M. M., Thusoo, E., Sharma, B. D., Getahun, F., Stevenson, D. M., Maranas, C. D., Olson, D. G., Lynd, L. R., & Amador‐Noguez, D. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.10.006 · Cited by 8
Li, G., Cao, H., & Xu, Y. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby022 · Cited by 8
Cellular determinants of metabolite concentration ranges
Eloundou-Mbebi, J. M. O., Küken, A., Basler, G., & Nikoloski, Z. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006687 · Cited by 8
Tomi-Andrino, C., Norman, R. O. J., Millat, T., Soucaille, P., Winzer, K., Barrett, D. A., King, J. R., & Kim, D. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007694 · Cited by 8
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BackgroundInfluentialSciolino, N., Reverdatto, S., Premo, A., Breindel, L., Yu, J.-C., Theophall, G. G., Burz, D. S., Liu, A., Sulchek, T., Schmidt, A. M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-26444-z · Cited by 9
“Importantly, t P is shorter than the characteristic time t ch = A/J 65,66 for the change in the total concentration of labeled metabolite.”
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“Metabolites were quantified by LC-MS following fast filtration (30).”
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Mardoukhi, M. S. Y., Rapp, J., Irisarri, I., Gunka, K., Link, H., Marienhagen, J., de Vries, J., Stülke, J., & Commichau, F. M. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.14429 · Cited by 7
“Therefore, it is not surprising that glutamate is the dominating metabolites in prokaryotic and eukaryotic cells (Bennett et al., 2009; Park et al., 2016).”
BackgroundMeier, S., Zahid, A. L. N., Jørgensen, L. R., Wang, K.-C., Jensen, P. R., & Jensen, P. R. (2024) · Molecules · doi:10.3390/molecules29174133 · Cited by 7
“Determination of metabolic energetics across the tree of life has indicated that absolute metabolite concentrations and Gibbs free energies ( ∆ G ) are largely conserved [1].”
BackgroundMethodResultAn evaluation of thermal tolerance in six tardigrade species in an active and dry state
Loeffelholz, J., Meese, E. K., Giovannini, I., Ullibarri, K., Momeni, S., Merfeld, N., Wessel, J., Guidetti, R., Rebecchi, L., & Boothby, T. C. (2024) · Biology Open · doi:10.1242/bio.060485 · Cited by 7
Venkataraman, Y. R., & Huffmyer, A. S. (2025) · Integrative and Comparative Biology · doi:10.1093/icb/icaf138 · Cited by 7
Sharma, B. D., Olson, D. G., Giannone, R. J., Hettich, R. L., & Lynd, L. R. (2023) · Applied and Environmental Microbiology · doi:10.1128/aem.00406-23 · Cited by 7
Combining systems and synthetic biology for in vivo enzymology
Castaño‐Cerezo, S., Chamas, A., Kulyk, H., Treitz, C., Bellvert, F., Tholey, A., Galéote, V., Camarasa, C., Heux, S., Garcia-Alles, L. F., et al. (2024) · The EMBO Journal · doi:10.1038/s44318-024-00251-w · Cited by 8
Metabolic coordination and phase transitions in spatially distributed multi-cellular systems
Narayanankutty, K., Pereiro-Morejón, J. A., Ferrero-Fernández, A., Onesto, V., Forciniti, S., del Mercato, L. L., Mulet, R., Martino, A. D., Tourigny, D. S., & Martino, D. D. (2025) · Communications Physics · doi:10.1038/s42005-025-02133-x · Cited by 8
Sanjeev, B. S., Chitara, D., & Madhumalar, A. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1993342 · Cited by 7
“The concentration of metabolites was within 6% of their physiological mean values (Park et al., 2016).”
BackgroundCEST MRI and MALDI imaging reveal metabolic alterations in the cervical lymph nodes of EAE mice
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) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02493-z · Cited by 7
An engineered biosensor enables dynamic aspartate measurements in living cells
Davidsen, K., Marvin, J. S., Aggarwal, A., Brown, T. A., & Sullivan, L. B. (2023) · eLife · doi:10.7554/elife.90024 · Cited by 15
Kurhaluk, N., Лукаш, О. В., & Tkaczenko, H. (2023) · Cellular Physiology and Biochemistry · doi:10.33594/000000669 · Cited by 7
“The finding that α-ketoglutarate has multiple functions in physiology, regulating epigenetic changes [16] with a α-ketoglutarate-to-succinate ratio, as shown by Carey et al. [17], may be associated with attractive therapeutic opportunities in treatment of diseases [18].”
BackgroundOn the Evolution of Biomolecular Condensates: From Prebiotic Origins to Subcellular Diversity
Hadarovich, A., Kuster, D., Romero, M. L. R., & Tóth-Petróczy, Á. (2025) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-101123-051723 · Cited by 9
Susceptibility and regulation of biomolecular condensates by solutes
Matsuzawa, T., Varma, K., Bate, T., Lorenz, C., Larina, K., Bauermann, J., Matthias, D., Grubić, T., Style, R. W., Steinmetz, M. O., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2604434123 · Cited by 5
Adiji, O. A., McConnell, B. S., & Parker, M. W. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae122 · Cited by 6
Saoud, M., Grau, J., Rennert, R. C., Mueller, T., Yousefi, M., Davari, M. D., Hause, B., Csük, R., Rashan, L. J., Große, I., et al. (2024) · Advanced Science · doi:10.1002/advs.202404085 · Cited by 6
Tracking fructose 1,6-bisphosphate dynamics in liver cancer cells using a fluorescent biosensor
Pérez-Chávez, I., Koberstein, J. N., Pueyo, J. M., Gilglioni, E. H., Vertommen, D., Baeyens, N., Ezeriņa, D., Gurzov, E. N., & Messens, J. (2024) · iScience · doi:10.1016/j.isci.2024.111336 · Cited by 6
Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells
Santiappillai, N. T., Cao, Y., Hakeem-Sanni, M. F., Yang, J. Y. H., Quek, L., & Hoy, A. J. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00099-0 · Cited by 6
“The approach draws upon thermodynamic principles in terms of reaction equilibrium and Gibbs energy (Park et al, 2016), and in practice, there is robust evidence for coordinated changes among proximal and hub metabolites (Martínez-Reyes and Chandel, 2020).”
An approach to learn regulation to maximize growth and entropy production rates in metabolism
King, E., Holzer, J., North, J. A., & Cannon, W. R. (2023) · Frontiers in Systems Biology · doi:10.3389/fsysb.2023.981866 · Cited by 6
Bradley, J. M., Bugg, Z., Sackey, A., Andrews, S. C., Wilson, M. T., Svistunenko, D. A., Moore, G. R., & Brun, N. E. L. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202401379 · Cited by 6
Key glycolytic branch influences mesocarp oil content in oil palm
Ruzlan, N., Low, Y. S. J., Win, W., Musa, N. A., Ong, A. L., Chew, F. T., Appleton, D., Yusof, H. M., & Kulaveerasingam, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-10195-3 · Cited by 6
Engineering microbial chemical factories using metabolic models
Sarkar, D., & Maranas, C. D. (2019) · BMC Chemical Engineering · doi:10.1186/s42480-019-0021-9 · Cited by 6
Thermodynamics shape the in vivo enzyme burden of glycolytic pathways
Khana, D. B., Jen, A., Shishkova, E., Thusoo, E., Williams, J., Henkel, A., Stevenson, D. M., Coon, J. J., & Amador-Noguez, D. (2025) · doi:10.1101/2025.01.31.635972 · Cited by 5
“These flux and thermody namic data were calculated under similar growth conditions used in this study (52–54).”
Role of Trifluoroacetic Acid Anions in pHP1α Liquid–Liquid Phase Separations
Chin, S. Y., Chen, Y., Zhao, L., Chng, C., Kuang, J., Zhai, Z., Lu, Y., Huang, C., Shi, C., Shi, X., et al. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c03218 · Cited by 5
Methods and Guidelines for Metabolism Studies: Applications to Cancer Research
Li, M., Amend, S. R., & Pienta, K. J. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26178466 · Cited by 6
“The authors identified that FBP was present in low micromolar concentrations in liver cancer cells, in contrary to previously hypothesized millimolar ranges [265,266].”
Qu, Y., Ren, S.-H., Liao, Y., Qiu, X., Tan, L., Song, C.-P., Sun, Y.-J., & Ding, C. (2026) · Autophagy · doi:10.1080/15548627.2026.2624746 · Cited by 5
“…network, the addition of glutamate or aspartate can enhance the replenishment flow of the TCA cycle, ensuring sufficient intermediates for the synthesis of amino acids and other biomolecules, which helps maintain the intracellular nitrogen balance and the interconversion of amino acids [70,71].”
Lanthanide-based luminescent probes for biological magnesium: accessing polyphosphate-bound Mg 2+
Pinto‐Pacheco, B., Lin, Q., Yan, C. W., Silva, S. D. M., & Buccella, D. (2023) · Chemical Communications · doi:10.1039/d2cc07095b · Cited by 5
Recruitment of a Middling Promiscuous Enzyme Drives Adaptive Metabolic Evolution in Escherichia coli
Campbell, R. P., Whittington, A. C., Zorio, D. A. R., & Miller, B. G. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad202 · Cited by 6
Yamamoto, M., Osanai, T., & Ito, S. (2024) · Plant Molecular Biology · doi:10.1007/s11103-024-01495-0 · Cited by 5
“5b), the absolute concentration of FBP in yeast (4 mM) is below 5 mM (Park et al. 2016).”
BackgroundInfluentialChemotranscriptomic profiling with a thiamine monophosphate photoaffinity probe
Crielaard, S., Peters, C. F. M., Slivkov, A. I., van den Homberg, D. A. L., & Velema, W. A. (2025) · Chemical Science · doi:10.1039/d4sc06189f · Cited by 5
Fermentative Production of 2,4-Dihydroxybutyrate from Glucose via a Redox Balanced Route
Zhang, J., Wang, Y., Wang, D., & Yu, B. (2025) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c00112 · Cited by 5
A geometric deep learning framework for genome-wide prediction of enzyme turnover number
Pan, T., Cui, X., Koh, H. Y., Bi, Y., Wang, X., Zhang, Y., Hu, S., Webb, G. I., Kurgan, L., Zhang, G., et al. (2026) · Genome biology · doi:10.1186/s13059-026-03986-3 · Cited by 8
“Currently, there are no high-throughput methods for experimentally measuring K cat values and the evaluation process remains time-consuming and labor-intensive [12].”
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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