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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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
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.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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 301–400 of 453
Guo, T., Sperber, A. M., Krieger, I. V., Duan, Y., Chemelewski, V. R., Sacchettini, J. C., & Herman, J. K. (2023) · Journal of Bacteriology · doi:10.1128/jb.00202-23 · Cited by 5
“Second, assuming an intracellular OAA concentration of 0.5 µM (the estimate for Escherichia coli ) (40), YisK-catalyzed decarboxylation would be around 30 times faster than the spontaneous rate observed in our reaction buffer (Fig.”
BackgroundInfluentialComputing Biomolecular System Steady-States
Gawthrop, P. J. (2017) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2017.2787486 · Cited by 6
“For example, study of the energetic behaviour of metabolic biomolecular systems at constant flow leads to conclusions about efficient enzyme utilisation [16].”
BackgroundKeller, M. A., Driscoll, P. C., Messner, C. B., & Ralser, M. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.12103.2 · Cited by 5
Evolutionary Aspects of the Oxido-Reductive Network of Methylglyoxal
Kalapos, M. P. (2021) · Journal of Molecular Evolution · doi:10.1007/s00239-021-10031-3 · Cited by 5
13C Flux Analysis in Biotechnology and Medicine
Cheah, Y. E., Hasenour, C. M., & Young, J. D. (2017) · Systems Biology · doi:10.1002/9783527696130.ch2 · Cited by 5
Yang, X., Ha, G., & Needleman, D. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.20.392225 · Cited by 5
Robustness of mitochondrial biogenesis and respiration explain aerobic glycolysisPreprint
Arunachalam, E., Keber, F. C., Law, R. C., Kumar, C. K., Shen, Y., Park, J. O., Wühr, M., & Needleman, D. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.04.601975 · Cited by 5
Microbes use transporters to regulate the release of metabolites based on valuePreprint
Sulheim, S., Broli, G., Doubleday, P. F., Gillon, A., Luneau, J. S., Padmanabha, P., Quinn, A., Ulrich, E., Vogel, M. A., Zamboni, N., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.19.671024 · Cited by 5
Activity-dependent citrate dynamics in neurons
Rosen, P. C., Fu, P., Ferrán, B., Kim, E. E., Brooks, D. J., Lim, D., Díaz‐García, C. M., & Yellen, G. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2519902122 · Cited by 4
“( 22 , 28 ).”
Ling, T., O’Brien, C., St‐Germain, J. R., Rondeau, V., Shi, M. Y., Berman, J. M., Cepa, A. K., Raez, P. S., Wunderlich, M., Carter, K. M., et al. (2025) · Blood · doi:10.1182/blood.2025028933 · Cited by 4
ATP in Mitochondria: Quantitative Measurement, Regulation, and Physiological Role
Lapashina, A. S., Tretyakov, D. O., & Feniouk, B. A. (2025) · Biochemistry (Moscow) · doi:10.1134/s0006297925603338 · Cited by 7
Metabolite control of enzyme activity links stress to biosynthetic regulation
van de Ven, W., Hur, M., Mendez, M. F. G., Guo, J., Ke, H., Mahmood, R. A., Kim, S., Sharkey, T. D., & Dehesh, K. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2529243123 · Cited by 4
“This principle aligns with systems - level metabolic theory, which posits that pathway throughput is shaped primarily by metabolite levels and allosteric feedback rather than by changes in enzyme abundance (39, 45).”
Lei, M., Song, B., Shi, X., Duan, A., Lu, D., Ren, Y., Zhang, Y., Shou, R., Li, H., Wang, Z., et al. (2025) · Experimental Neurology · doi:10.1016/j.expneurol.2025.115326 · Cited by 4
A missing enzyme-rescue metabolite as cause of a rare skeletal dysplasia
Jacobs, J., Lyubenova, H., Potelle, S., Kopp, J., Gerin, I., Chan, W. L., Santos, M. R. D. L., Hülsemann, W., Mensah, M. A., Cormier‐Daire, V., et al. (2025) · Nature · doi:10.1038/s41586-025-09397-x · Cited by 5
“This reaction has a Michaelis constant ( K M ) of approximately 60 µM with respect to UDP-glucose, thus we expect that its activity is saturated at concentrations of this substrate in the endoplasmic reticulum and the Golgi apparatus 31,32 .”
Fan, W., Hao, Y., Hou, X., Ding, C., Huang, D., Zheng, W., & Dai, Z. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101393 · Cited by 4
Cannon, W. R., Britton, S. R., Banwarth-Kuhn, M., & Alber, M. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0180417 · Cited by 4
“• Condition 2 : If the boundary conditions are the same for every state such that the total drop in entropy is Z λ but the states have different steady state rates, it is not necessarily the case that the state obeying Eqns 39 and 40 will have the maximum entropy production rate dSdt .”
BackgroundInfluentialMetabolic Energy is Stored in a Homeostatic Trans-Membrane Water Barochemical Gradient
SPRINGER, C. S. J., Pike, M. M., & Barbara, T. M. (2025) · The Journal of Membrane Biology · doi:10.1007/s00232-024-00332-1 · Cited by 4
“The cellular glucose consumption rate, c MR glc (consump), has been determined to be 38 μM(glc)/s/ cell for murine kidney epithelial cells (Park et al. 2016) and for brain tissue cells, assuming reasonable cell density and volume values (DiNuzzo et al. 2024).”
InfluentialInformation Gain Limit of Biomolecular Computation
Arunachalam, E., & Lin, M. M. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.148401 · Cited by 4
Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
Seep, L., Razaghi‐Moghadam, Z., & Nikoloski, Z. (2021) · Scientific Reports · doi:10.1038/s41598-021-87643-8 · Cited by 4
Sannelli, F., Wang, K.-C., Jensen, P. R., & Meier, S. (2023) · Analytical Methods · doi:10.1039/d3ay01120h · Cited by 4
Gawthrop, P. J., Pan, M., & Crampin, E. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.24.436792 · Cited by 4
Optical metabolic imaging of the tricarboxylic acid cyclePreprint
Chadha, R. S., Kocheril, P. A., Colazo, A., Kapelczak, E. D., Suen, B., Yang, Z., TeSlaa, T. A., & Wei, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.28.702395 · Cited by 5
“…SRS and LC-MS, informed by physics-425 based modeling) are broadly generalizable to Raman metabolic imaging, and continued technical developments 51 may grant much improved sensitivity to allow investigations at more physiologically relevant concentrations (such as ~5.8 mM for pyruvate 23 ).”
InfluentialSu, Z., Shi, C., Fan, Y., Gao, Y., Wang, L., Ren, X., Shen, Y., & Wang, M. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.138443 · Cited by 3
Malic enzyme-based system for transhydrogenation between nicotinamide cofactors
Xue, H., Hu, Y., Shah, A. M., Wang, X., Guo, X., Huang, Y., & Zhao, Z. K. (2025) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2025.08.009 · Cited by 3
“Because pyruvate is a central metabolite at relatively high concentrations [27], for example, over 3.0 mM in Escherichia coli cells and 8.0 mM in Saccharomyces cerevisiae cells, it can provide a driving force for the oxidative half of the process and receive electrons from the reduced cofactor X –…”
InfluentialSingle-cell imaging of liver metabolic dynamics using fluorescent biosensors
Pérez-Chávez, I., Gilglioni, E. H., Ezeriņa, D., Messens, J., & Gurzov, E. N. (2025) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2025.10.003 · Cited by 4
Single‐Cell Metabolic Imaging Reveals Glycogen‐Driven Adaptations in Endothelial Cells
Chadha, R. S., Yang, B. K., Yuan, D., Kapelczak, E. D., Kocheril, P. A., Tapia, A., Mahajan, S., Colazo, A., Malhi, N. K., Ambarian, J. A., et al. (2025) · Advanced Science · doi:10.1002/advs.202508517 · Cited by 3
Engineering and characterization of small-molecule transporters for cell factories
Zhang, Y., Kell, D. B., & Borodina, I. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.11.019 · Cited by 3
Moonlighting in metabolism: bifunctional enzymes control nitrogen metabolism in Bacillus subtilis
Gibhardt, J., Lieber, M. M., Götz, C. S., Völker, F., Blank, L. M., Forchhammer, K., Jayaraman, V., Noda‐García, L., & Commichau, F. M. (2026) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00430-25 · Cited by 3
“In many bacteria, including the Gram-positive and ram-negative model bacteria Bacillus subtilis and Escherichia coli , respectively, it has been shown that glutamate comprises between 20% and 40% of the total molar concentration of the measured intracellular metabolome (2–9).”
Metabolite glues as a means of purine sensing and chemotherapeutic response
Witus, S. R., Kober, M. M., Roh, H., Yang, Z., Choueiry, F., Ghate, A. S., Titov, D. V., & Rapé, M. (2026) · Nature · doi:10.1038/s41586-026-10790-3 · Cited by 6
“This interaction is crucial for PPAT feedback inhibition, allowing cells to detect purines at micromolar levels that reflect endogenous purine monophosphate concentrations 47 of 10–40 µM.”
Microbial Metabolomics: An Overview of Applications
van der Velden, P. M. M., & Jansen, R. S. (2023) · Springer eBooks · doi:10.1007/978-3-031-39094-4_6 · Cited by 3
Matsuda, F., Kamiyama, A., Yamasaki, K., Seike, T., & Okahashi, N. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c04480 · Cited by 3
Keller, M. A., Driscoll, P. C., Messner, C. B., & Ralser, M. (2017) · Wellcome Open Research · doi:10.12688/wellcomeopenres.12103.1 · Cited by 3
Back flux during anaerobic oxidation of butane supports archaea-mediated alkanogenesis
Chen, S., Chen, S., Musat, N., Kümmel, S., Ji, J., Lund, M., Gilbert, A. R., Lechtenfeld, O. J., Richnow, H. H., & Musat, F. (2024) · Nature Communications · doi:10.1038/s41467-024-53932-9 · Cited by 3
A Mathematical Model of HIF-1 Regulated Cellular Energy Metabolism
Bocharov, G. A., Jäger, W., Knoch, J., Neuss‐Radu, M., & Thiel, M. (2020) · Vietnam Journal of Mathematics · doi:10.1007/s10013-020-00426-y · Cited by 3
Systematic analysis of transcriptional and post-transcriptional regulation of metabolism in yeastPreprint
Gonçalves, E., Nakic, Z. R., Zampieri, M., Wagih, O., Ochoa, D., Sauer, U., Beltrão, P., & Sáez-Rodríguez, J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/057398 · Cited by 3
Glutathione supports lipid abundance in vivoPreprint
Asantewaa, G., Tuttle, E. T., Ward, N. P., Kang, Y. P., Kim, Y., Kavanagh, M. E., Girnius, N., Chen, Y., Duncan, R. D., Rodriguez, K., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.10.524960 · Cited by 3
Mardoukhi, M. S. Y., Rapp, J., Irisarri, I., Gunka, K., Link, H., Marienhagen, J., de Vries, J., Stülke, J., & Commichau, F. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.10.566560 · Cited by 3
Pan, T., Cui, X., Koh, H. Y., Bi, Y., Wang, X., Zhang, Y., Hu, S., Webb, G. I., Kurgan, L., Zhang, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.09.642294 · Cited by 3
“# values and the evaluation process remains time-consuming and labor-intensive ( 12 ).”
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 · doi:10.1002/ange.202401379 · Cited by 2
Kolchinsky, A., Dechant, A., Yoshimura, K., & Ito, S. (2026) · Physical Review Research · doi:10.1103/r48t-dghl · Cited by 7
“The standard Gibbs free energies are reported for pH 7.3; we perform a correction to account for measured cytosolic pH values [125]: 7.7 for E. coli , 7.2 for yeast, and 7.2 for mammalian cells.”
InfluentialZhou, N., Mendonca, C. M., Carroll, A. L., Pate, S. C., Nieto‐Domínguez, M., Chen, X., Zhang, L., Teitel, K. P., Dekker, N. K., Elmore, J. R., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2513479122 · Cited by 2
Yoshidome, D., Kuze, K., Ichiyanagi, A., Yoshida, A., Kosono, S., & Nishiyama, M. (2025) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-025-13646-4 · Cited by 2
“Then, we calculated the putative reaction rate of CS in the CS + CitS strain using literature values of 0.5 µM OAA and 600 µM acetyl-CoA for E. coli cells (Park et al. 2016), and 3640 µM 2-OG, as determined in the present study.”
InfluentialA Flawed Conjecture Keeps Haunting Brain Energy Metabolism Research
Schurr, A. (2026) · NeuroSci · doi:10.3390/neurosci7030049 · Cited by 3
“Either prefix, aerobic or anaerobic, are not necessary to describe the pathway that, thermodynamically (see below), should end with lactate [34,35].”
InfluentialReductionism in the study of enzyme adaptation
Fields, P. A. (2021) · Comparative Biochemistry and Physiology Part B Biochemistry and Molecular Biology · doi:10.1016/j.cbpb.2021.110574 · Cited by 2
An Isotonic Model of Neuron Swelling Based on Co-Transport of Salt and Water
Maex, R. (2023) · Membranes · doi:10.3390/membranes13020206 · Cited by 2
Sensitivity analysis of biochemical systems using bond graphs
Gawthrop, P. J., & Pan, M. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0192 · Cited by 1
Newton-Tanzer, E., Can, S. N., Demmelmair, H., Horak, J., Holdt, L. M., Koletzko, B. V., & Grote, V. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae599 · Cited by 2
“In addition, at lower protein concentrations, certain metabolic pathways might be less ac - tive or reach a maximum rate of reaction, so that higher protein may alter kinetics and lead to different breakpoints, activation thresholds, or even the saturation of pathways (50).”
Mori, K., Seike, T., Okahashi, N., Takahashi, T., & Matsuda, F. (2025) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2025.04.001 · Cited by 2
Plazolles, N., Kulyk, H., Cahoreau, E., Biran, M., Wargnies, M., Pineda, E., Kadri, M. E., Rimoldi, A., Hervé, P., Asencio, C., et al. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3002938 · Cited by 2
“For instance, the reverse gluconeogenic flux of PFK in kidney cells was estimated to be less than 0.7% of the glycolytic forward flux [6].”
InfluentialCai, Y. A., Liu, N., & Zhu, Z. (2018) · BIO-PROTOCOL · doi:10.21769/bioprotoc.3015 · Cited by 2
Nutrient Availability Impacts Intracellular Metabolic Profiles in Digital Organisms
Kannan, K. S. S., Patil, T., Vij, R., Loh, B. J., & Ling, M. H. (2022) · ACTA SCIENTIFIC MICROBIOLOGY · doi:10.31080/asmi.2022.05.1076 · Cited by 1
Piljukov, V., Sillamaa, S., Sedman, T., Garber, N., Rätsep, M., Freiberg, A., & Sedman, J. (2023) · Mitochondrion · doi:10.1016/j.mito.2023.02.004 · Cited by 2
Drug Cocktail Formulation via Circuit Design
Beahm, D. R., Deng, Y., DeAngelo, T. M., & Sarpeshkar, R. (2023) · IEEE Transactions on Molecular Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2023.3246928 · Cited by 2
A Divalent Metal Cation–Metabolite Interaction Model Reveals Cation Buffering and Speciation
Sieg, J. P. (2024) · Biochemistry · doi:10.1021/acs.biochem.4c00125 · Cited by 2
Tjhin, E. T., Spry, C., Sewell, A. L., Hoegl, A., Barnard, L., Sexton, A. E., Howieson, V. M., Maier, A. G., Creek, D. J., Strauss, E., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/137182 · Cited by 2
Extracellular Domains of CAR Reprogram T-Cell Metabolism Without Antigen StimulationPreprint
Lakhani, A., Chen, X., Chen, L. C., Khericha, M., Chen, Y. Y., & Park, J. O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.03.533021 · Cited by 2
An engineered biosensor enables dynamic aspartate measurements in living cellsPreprint
Davidsen, K., Marvin, J. S., Aggarwal, A., Brown, T. A., & Sullivan, L. B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.27.546775 · Cited by 3
Slenter, D. (2024) · Research Publications (Maastricht University) · doi:10.26481/dis.20240624ds · Cited by 2
Overflow metabolism originates from growth optimization and cell heterogeneityPreprint
Wang, X. (2025) · eLife · doi:10.7554/elife.94586.3 · Cited by 2
HIF ‐1α signaling pathway is related with glycolysis in odontogenic keratocysts at single‐cell level
Man, Q. (2023) · Oral Diseases · doi:10.1111/odi.14636 · Cited by 1
Liu, Y., Zhang, X., Yang, E., Liu, X., Su, W., Wang, Z., & Wang, H. (2024) · Biomolecules · doi:10.3390/biom14111440 · Cited by 1
Yu, J.-C., Sciolino, N., Breindel, L., Lin, Q., Burz, D. S., & Shekhtman, A. G. (2025) · Biochemistry · doi:10.1021/acs.biochem.5c00086 · Cited by 1
Seike, T., Kahar, P., Ogino, C., & Matsuda, F. (2025) · Mass Spectrometry · doi:10.5702/massspectrometry.a0177 · Cited by 1
Lindahl, P. A., & Walton, J. R. (2025) · BioMetals · doi:10.1007/s10534-025-00758-7 · Cited by 2
“S3) were estimated from reported values (Jewison et al. 2012; Cai et al. 2011; Boer et al. 2010; Park et al. 2016).”
Yin, J., Du, Z., Liu, X., Lu, C., Wang, X. H., Chen, Y., You, Y., Zhang, J., Ge, X., & Yan, W. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218384 · Cited by 1
Beyond Correlation: Constraint Architecture Explains Proteome–Metabolome Decoupling
Kim, K. D., & Yoo, B. C. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27093971 · Cited by 2
“The mapping from protein abundance to metabolite pools is mediated by enzyme activity, thermodynamic constraints, and network topology [5,13].”
InfluentialChen, J., Yi-ran, H., & Zhong, C. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.104981 · Cited by 1
Free-energy transduction mechanisms shape the flux space of metabolic networks
Pfeuty, B. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.09.012 · Cited by 3
Energy transduction in complex networks with multiple resources: The chemistry paradigm
Bilancioni, M., & Esposito, M. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0280649 · Cited by 1
The thermophilic route to succinic acid
Koendjbiharie, J. G. (2020) · doi:10.18174/510683 · Cited by 1
Reznik, E., Christodoulou, D., Goldford, J. E., Briars, E. A., Sauer, U., Segrè, D., & Noor, Ε. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3155788 · Cited by 1
Alsiyabi, A., Brown, B., Immethun, C. M., Wilkins, M. R., & Saha, R. (2021) · Research Square · doi:10.21203/rs.3.rs-334477/v1 · Cited by 1
Chen, M., Xie, T., Li, H., Zhuang, Y., Xia, J., & Nielsen, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1900686/v1 · Cited by 1
Universal Design Principle to Enhance Enzymatic Activity using the Substrate AffinityPreprint
Ooka, H., Chiba, Y., & Nakamura, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.01.526728 · Cited by 1
“10 Validation based on Experimental Data Finally, to evaluate whether Km = S can rationalize enzymatic properties in nature, we have analyzed their relationship based on the experimental data from Park et al.[35] The original data consisted of Km values of wild-type enzymes obtained from BRENDA, and intracellular S va…”
BackgroundMethodInfluentialAccurate and rapid determination of metabolic flux by deep learning of isotope patternsPreprint
Law, R. C., Pande, K., O’Keeffe, S., Nurwono, G., Ki, R., Lai, P., & Park, J. O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.06.565907 · Cited by 1
A global view of T cell metabolism in Systematic Lupus ErythematosusPreprint
Goetz, A., Morel, L. M., & Dixit, P. D. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-00hdq · Cited by 1
SPRINGER, C. S. J., Pike, M. M., & Barbara, T. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.17.589812 · Cited by 1
Thermodynamics Underpinning the Microbial Community-Level Nitrogen NetworksPreprint
Seto, M., Sasaki, R., Ooka, H., & Nakamura, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.21.595099 · Cited by 1
Widespread natural selection on metabolite levels in humansPreprint
Timasheva, Y., Lepik, K., Liska, O., Papp, B., & Kutalik, Z. (2024) · Genome Research · doi:10.1101/gr.278756.123 · Cited by 1
“First, evolutionarily distant species show substantial similarities in metabolite levels, indicating widespread evolutionary conservation of the metabolome (Ma et al. 2015; Park et al. 2016).”
O’Keeffe, S., Sim, H., McCue, E., & Park, J. O. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.09.658709 · Cited by 1
“In particular, absolute metabolite concentrations in primary cells and tissues have been inaccessible due to low availability of biological materials.”
InfluentialToumpe, I., Masid, M., Vassily Hatzimanikatis, A., & Mišković, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.22.682756 · Cited by 2
Arp, N. L., Deng, F., Lika, J., Seim, G. L., Cobra, P. F., Fritz, C. M., John, S. V., Rathinaraj, S., Shields, B. E., Amador‐Noguez, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.18.712640 · Cited by 1
Kokaji, T., Hatano, A., Ito, Y., Yugi, K., Eto, M., Ohno, S., Fujii, M., Hironaka, K., Egami, R., Inoue, H., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/653758
Essentiality of local topology and regulation in kinetic metabolic modelingPreprint
Li, G., Du, W., & Cao, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/806703
Puschmann, R. (2020) · edoc Publication server (Humboldt University of Berlin) · doi:10.18452/21044
Machine Learning and Optimal Control of Enzyme Activities to Preserve Solvent Capacity in the CellPreprint
Britton, S. R., Alber, M., & Cannon, W. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.06.028035
Bond Graphs Unify Stoichiometric Analysis and ThermodynamicsPreprint
Gawthrop, P. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15917
“The reaction free energies Φ were extracted from Table 4 provided by Park et al. (2016).”
BackgroundMethodProfiling the Metabolism of Human Cells by Deep C LabelingPreprint
Grankvist, N., Watrous, J. D., Lehmann, K. A., Lyutvinskiy, Y., Jain, M., & Nilsson, R. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3152002
Catalytic Bias in Enzymatic Metal Cofactor‐Based Oxidation–Reduction Catalysis
Varghese, F., Mulder, D. W., Raugei, S., Zadvornyy, O. A., King, P. W., & Peters, J. W. (2021) · Encyclopedia of Inorganic and Bioinorganic Chemistry · doi:10.1002/9781119951438.eibc2770
Packed Cell Volume is an Overestimate in Common Cancer Cell Lines
Engström, I., Davidsen, K., & Sullivan, L. B. (2021) · BIO-PROTOCOL · doi:10.21769/bioprotoc.4216
Zamboni, N., & Cherkaoui, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.3929/ethz-b-000511784
Wibowo, A. (2014) · SKRIPSI Jurusan Teknik Mesin - Fakultas Teknik UM
Hammond, M. C., & Su, Y. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734655915.793565125
Tanaka, K., Shirai, T., Vavricka, C. J., Matsuda, M., Kondo, A., & Hasunuma, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.04.486922
Sahin, A., Weilandt, D., & Vassily Hatzimanikatis, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.12.488028
Corrigan, R. M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734655915.793554952
The dodecin of Mycobacterium tuberculosis : biological function and biotechnical applications
Bourdeaux, F. A. (2021) · Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · doi:10.21248/gups.60946
Faculty Opinions recommendation of Four key steps control glycolytic flux in mammalian cells.
Kuroda, S., & Ohno, S. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.733562817.793549184
Faculty Opinions recommendation of Four key steps control glycolytic flux in mammalian cells.
Olson, A. L. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.733562817.793575560
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
