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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Average metabolite concentrations generated from the model 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.
“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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Experimental — accuracy not yet measuredThe main deviation in glycolysis is 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.
“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
The Results section (‘Integrating metabolite concentrations and free energies’; page unavailable) says aldolase was closer to equilibrium in yeast, supporting a smaller free-energy drop qualitatively. It does not give an aldolase-specific value below 10 kJ/mol. The roughly 25 kJ/mol kinetic estimate is the citing authors’ comparison, not a result attributed to Park et al. (LLM, conf=0.76)
Experimental — accuracy not yet measuredOther studies have reported overall free energy changes through glycolysis from −30 to −70 kJ/mol.
“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
The Results section reports an average cumulative glycolytic free-energy change of −46 kJ/mol and says the drop in yeast was nearly double that in mammalian cells. This supports variation in the overall change, but our copy does not give the claimed −30 to −70 kJ/mol endpoints. The measured sequence also runs from phosphoglucose isomerase to pyruvate kinase, rather than including every step of glycolysis. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOnly relative metabolomics data on Syn3A is available, so metabolite concentrations used to initialize simulations were estimated from scaling of an E. coli study and a limited list from M. pneumoniae.
“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
High concentrations of phosphate reported in yeast may be inaccurate for Syn3A.
“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 · see citing works
A postmodern moral tale: the ethics of research relationships
Somerville, M. (2002) · Nature reviews. Drug discovery · doi:10.1038/nrd773 · Cited by 2,175 · see citing works
Fundamentals of Enzyme Kinetics
Cornish-Bowden, A. J. (1979) · Elsevier eBooks · doi:10.1016/c2013-0-04130-8 · Cited by 2,079 · see citing works
Physiological concentrations of purines and pyrimidines
Traut, T. W. (1994) · Molecular and Cellular Biochemistry · doi:10.1007/bf00928361 · Cited by 1,867 · see citing works
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 · see citing works
Feist, 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 1,367 · see citing works
Understanding the control of metabolism
(1997) · Choice Reviews Online · doi:10.5860/choice.34-6252 · Cited by 1,230 · see citing works
The Regulation of Cellular Systems
Heinrich, R., & Schuster, S. (1996) · Springer eBooks · doi:10.1007/978-1-4613-1161-4 · Cited by 1,224 · see citing works
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 1,005 · see citing works
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 · see citing works
Geiger, T., Wehner, A., Schaab, C., Cox, J., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.014050 · Cited by 797 · see citing works
Optimality and evolutionary tuning of the expression level of a protein
Dekel, E., & Alon, U. (2005) · Nature · doi:10.1038/nature03842 · Cited by 766 · see citing works
LEONARDI, R., Zhang, Y., ROCK, C., & Jackowski, S. (2005) · Progress in Lipid Research · doi:10.1016/j.plipres.2005.04.001 · Cited by 649 · see citing works
Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress
Ralser, M., Wamelink, M. M. C., Kowald, A., Gerisch, B., Heeren, G., Struys, E. A., Klipp, E., Jakobs, C., Breitenbach, M., Lehrach, H. R., et al. (2007) · Journal of Biology · doi:10.1186/jbiol61 · Cited by 627 · see citing works
Thermodynamics-Based Metabolic Flux Analysis
Henry, C. S., Broadbelt, L. J., & Vassily Hatzimanikatis, A. (2006) · Biophysical Journal · doi:10.1529/biophysj.106.093138 · Cited by 608 · see citing works
Bajad, S. U., Lu, W., Kimball, E., Yuan, J., Peterson, C. N., & Rabinowitz, J. D. (2006) · Journal of Chromatography A · doi:10.1016/j.chroma.2006.05.019 · Cited by 593 · see citing works
BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions
Schellenberger, J., Park, J. O., Conrad, T. M., & Palsson, B. Ø. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-213 · Cited by 585 · see citing works
Dynamic modeling of the central carbon metabolism of Escherichia coli
Chassagnole, C., Noisommit‐Rizzi, N., Schmid, J. W., Mauch, K., & Reuß, M. (2002) · Biotechnology and Bioengineering · doi:10.1002/bit.10288 · Cited by 553 · see citing works
Glycolytic strategy as a tradeoff between energy yield and protein cost
Flamholz, A., Noor, E., Bar-Even, A., Liebermeister, W., & Milo, R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215283110 · Cited by 531 · see citing works
LC‐MS Data Processing with MAVEN: A Metabolomic Analysis and Visualization Engine
Clasquin, M. F., Melamud, E., & Rabinowitz, J. D. (2012) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1411s37 · Cited by 507 · see citing works
Lu, W., Clasquin, M. F., Melamud, E., Amador‐Noguez, D., Caudy, A. A., & Rabinowitz, J. D. (2010) · Analytical Chemistry · doi:10.1021/ac902837x · Cited by 504 · see citing works
Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements
Antoniewicz, M. R., Kelleher, J. K., & Stephanopoulos, G. N. (2006) · Metabolic Engineering · doi:10.1016/j.ymben.2006.01.004 · Cited by 451 · see citing works
Fan, J., Kamphorst, J. J., Mathew, R., Chung, M. K., White, E., Shlomi, T., & Rabinowitz, J. D. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.65 · Cited by 428 · see citing works
Schomburg, I., Chang, A., Placzek, S., Söhngen, C., Rother, M., Lang, M., Munaretto, C., Ulas, S., Stelzer, M., Grote, A., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1049 · Cited by 418 · see citing works
Visual account of protein investment in cellular functions
Liebermeister, W., Noor, Ε., Flamholz, A. I., Davidi, D., Bernhardt, J., & Milo, R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314810111 · Cited by 408 · see citing works
Absolute quantitation of intracellular metabolite concentrations by an isotope ratio-based approach
Bennett, B. D., Yuan, J., Kimball, E., & Rabinowitz, J. D. (2008) · Nature Protocols · doi:10.1038/nprot.2008.107 · Cited by 384 · see citing works
Beg, Q. K., Vazquez, A., Ernst, J., de Menezes, M. A., Bar-Joseph, Z., Barabási, A.-L., & Oltvai, Z. N. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609845104 · Cited by 368 · see citing works
Acidic Acetonitrile for Cellular Metabolome Extraction from Escherichia coli
Rabinowitz, J. D., & Kimball, E. (2007) · Analytical Chemistry · doi:10.1021/ac070470c · Cited by 346 · see citing works
Cross-Platform Comparison of Methods for Quantitative Metabolomics of Primary Metabolism
Buescher, J. M., Czernik, D., Ewald, J. C., Sauer, U., & Zamboni, N. (2009) · Analytical Chemistry · doi:10.1021/ac8022857 · Cited by 324 · see citing works
NIELSEN, J. K. (2001) · Applied Microbiology and Biotechnology · doi:10.1007/s002530000511 · Cited by 315 · see citing works
Putative regulatory sites unraveled by network‐embedded thermodynamic analysis of metabolome data
Kümmel, A., Panke, S., & Heinemann, M. (2006) · Molecular Systems Biology · doi:10.1038/msb4100074 · Cited by 290 · see citing works
Consistent Estimation of Gibbs Energy Using Component Contributions
Noor, Ε., Haraldsdóttir, H. S., Milo, R., & Fleming, R. M. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003098 · Cited by 288 · see citing works
Mashego, M. R., Wu, L., van Dam, J. C., Ras, C., Vinke, J. L., van Winden, W. A., van Gulik, W. M., & Heijnen, J. J. (2004) · Biotechnology and Bioengineering · doi:10.1002/bit.10907 · Cited by 258 · see citing works
Compounds Which Serve as the Sole Source of Carbon or Nitrogen for Salmonella typhimurium LT-2
Gutnick, D. L., Calvo, J. M., Kłopotowski, T., & Ames, B. N. (1969) · Journal of Bacteriology · doi:10.1128/jb.100.1.215-219.1969 · Cited by 251 · see citing works
Ensemble Modeling of Metabolic Networks
Tran, L. M., Rizk, M. L., & Liao, J. C. (2008) · Biophysical Journal · doi:10.1529/biophysj.108.135442 · Cited by 249 · see citing works
Elimination of Thermodynamically Infeasible Loops in Steady-State Metabolic Models
Schellenberger, J., Lewis, N. E., & Palsson, B. Ø. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2010.12.3707 · Cited by 216 · see citing works
Sundararaj, S. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh108 · Cited by 211 · see citing works
Genome-Scale Thermodynamic Analysis of Escherichia coli Metabolism
Henry, C. S., Jankowski, M. D., Broadbelt, L. J., & Vassily Hatzimanikatis, A. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.071720 · Cited by 208 · see citing works
13CFLUX2—high-performance software suite for 13C-metabolic flux analysis
Weitzel, M., Nöh, K., Dalman, T., Niedenführ, S., Stute, B., & Wiechert, W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts646 · Cited by 202 · see citing works
Xu, Y., Lu, W., & Rabinowitz, J. D. (2015) · Analytical Chemistry · doi:10.1021/ac504118y · Cited by 202 · see citing works
Nöh, K., Grönke, K., Luo, B., Takors, R., Oldiges, M., & Wiechert, W. (2006) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2006.11.015 · Cited by 200 · see citing works
Thermodynamic constraints for biochemical networks
Beard, D. A., Babson, E. K., Curtis, E. B., & Qian, H. (2004) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.01.008 · Cited by 193 · see citing works
Measuring enzyme activities under standardized in vivo ‐like conditions for systems biology
van Eunen, K., Bouwman, J., Daran-Lapujade, P. A. S., Postmus, J., Canelas, A. B., Mensonides, F. I. C., Orij, R., Tüzün, I., van den Brink, J., Smits, G. J., et al. (2010) · FEBS Journal · doi:10.1111/j.1742-4658.2009.07524.x · Cited by 178 · see citing works
Hoppe, A., Hoffmann, S., & Holzhütter, H. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-23 · Cited by 158 · see citing works
Relationship between Thermodynamic Driving Force and One-Way Fluxes in Reversible Processes
Beard, D., & Qian, H. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000144 · Cited by 153 · see citing works
Valgepea, K., Adamberg, K., Seiman, A., & Vilu, R. (2013) · Molecular BioSystems · doi:10.1039/c3mb70119k · Cited by 145 · see citing works
Kinetic flux profiling of nitrogen assimilation in Escherichia coli
Yuan, J., Fowler, W. U., Kimball, E., Lu, W., & Rabinowitz, J. D. (2006) · Nature Chemical Biology · doi:10.1038/nchembio816 · Cited by 125 · see citing works
Isotopomer studies of gluconeogenesis and the Krebs cycle with 13C-labeled lactate.
Katz, J. J., Wals, P. A., & Lee, W. N. (1993) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(19)74421-4 · Cited by 120 · see citing works
Fleming, R. M. T., Thiele, I., & Nasheuer, H. (2009) · Biophysical Chemistry · doi:10.1016/j.bpc.2009.08.007 · Cited by 83 · see citing works
Ultrasensitive regulation of anapleurosis via allosteric activation of PEP carboxylase
Xu, Y., Amador‐Noguez, D., Reaves, M. L., Feng, X., & Rabinowitz, J. D. (2012) · Nature Chemical Biology · doi:10.1038/nchembio.941 · Cited by 79 · see citing works
Berg, J. M., Tymoczko, J. L., & Stryer, L. (2013) · Springer eBooks · doi:10.1007/978-3-8274-2989-6 · Cited by 77 · see citing works
Metabolic dynamics in the human red cell. Part III—Metabolic reaction rates
Joshi, A., & Palsson, B. Ø. (1990) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(05)80012-8 · Cited by 76 · see citing works
Thermodynamic considerations on erythrocyte glycolysis
Minakami, S., & Yoshikawa, H. (1965) · Biochemical and Biophysical Research Communications · doi:10.1016/0006-291x(65)90711-4 · Cited by 58 · see citing works
A Mouse Model System to Genetically Dissect the Molecular Mechanisms Regulating Tumorigenesis
Degenhardt, K., & White, E. (2006) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-06-0439 · Cited by 54 · see citing works
Haraldsdóttir, H. S., Thiele, I., & Fleming, R. M. T. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.02.032 · Cited by 53 · see citing works
The Thermodynamic Meaning of Metabolic Exchange Fluxes
Wiechert, W. (2007) · Biophysical Journal · doi:10.1529/biophysj.106.099895 · Cited by 49 · see citing works
Vojinovic, V., & von Stockar, U. (2009) · Biotechnology and Bioengineering · doi:10.1002/bit.22309 · Cited by 48 · see citing works
Time hierarchy in enzymatic reaction chains resulting from optimality principles
Schuster, S., & Heinrich, R. (1987) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(87)80012-7 · Cited by 44 · see citing works
Phosphate exchange in the pit transport system in Escherichia coli
Rosenberg, H., Russell, L. M., Jacomb, P. A., & Chegwidden, K. (1982) · Journal of Bacteriology · doi:10.1128/jb.149.1.123-130.1982 · Cited by 35 · see citing works
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Martínez-Reyes, I., & Chandel, N. S. (2020) · Nature Communications · doi:10.1038/s41467-019-13668-3 · Cited by 2,813 · see citing works
Role of Glutathione in Cancer: From Mechanisms to Therapies
Kennedy, L., Sandhu, J. K., Harper, M.-E., & Cuperlovic-Culf, M. (2020) · Biomolecules · doi:10.3390/biom10101429 · Cited by 880 · see citing works
Metabolomics and Isotope Tracing
Jang, C., Chen, L., & Rabinowitz, J. D. (2018) · Cell · doi:10.1016/j.cell.2018.03.055 · Cited by 841 · see citing works
Glutamine Metabolism in Cancer: Understanding the Heterogeneity
Cluntun, A. A., Lukey, M. J., Cerione, R. A., & Locasale, J. W. (2017) · Trends in Cancer · doi:10.1016/j.trecan.2017.01.005 · Cited by 796 · see citing works
Fast automated reconstruction of genome-scale metabolic models for microbial species and communities
Machado, D., Andrejev, S., Tramontano, M., & Patil, K. R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky537 · Cited by 724 · see citing works
The pentose phosphate pathway in health and disease
TeSlaa, T. A., Ralser, M., Fan, J., & Rabinowitz, J. D. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00863-2 · Cited by 690 · see citing works
Robinson, J. L., Kocabaş, P., Wang, H., Cholley, P.-E., Cook, D., Nilsson, A., Anton, M., Ferreira, R., Domenzain, I., Billa, V., et al. (2020) · Science Signaling · doi:10.1126/scisignal.aaz1482 · Cited by 442 · see citing works
Metabolite Measurement: Pitfalls to Avoid and Practices to Follow
Lu, W., Su, X., Klein, M. S., Lewis, I. A., Fiehn, O., & Rabinowitz, J. D. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044952 · Cited by 457 · see citing works
Liu, X., & Locasale, J. W. (2017) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2017.01.004 · Cited by 453 · see citing works
Four Key Steps Control Glycolytic Flux in Mammalian Cells
Tanner, L. B., Goglia, A. G., Wei, M. H., Sehgal, T., Parsons, L. R., Park, J. O., White, E., Toettcher, J. E., & Rabinowitz, J. D. (2018) · Cell Systems · doi:10.1016/j.cels.2018.06.003 · Cited by 368 · see citing works
Guo, J. Y., Teng, X., Laddha, S. V., Ma, S., Nostrand, S. C. V., Yang, Y., Khor, S., Chan, C. S., Rabinowitz, J. D., & White, E. (2016) · Genes & Development · doi:10.1101/gad.283416.116 · Cited by 373 · see citing works
Multiscale Modeling Meets Machine Learning: What Can We Learn?
Peng, G. C. Y., Alber, M., Tepole, A. B., Cannon, W. R., De, S., Durá-Bernal, S., Garikipati, K., Karniadakis, G. E., Lytton, W. W., Perdikaris, P. G., et al. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09405-5 · Cited by 358 · see citing works
Aspartate is an endogenous metabolic limitation for tumour growth
Sullivan, L. B., Luengo, A., Danai, L. V., Bush, L. N., Diehl, F. F., Hosios, A. M., Lau, A. N., Elmiligy, S., Malstrom, S. E., Lewis, C. A., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0125-0 · Cited by 339 · see citing works
Finley, L. W. S. (2023) · Cell · doi:10.1016/j.cell.2023.01.038 · Cited by 331 · see citing works
Systems-level analysis of mechanisms regulating yeast metabolic flux
Hackett, S. R., Zanotelli, V. R., Xu, W., Goya, J., Park, J. O., Perlman, D. H., Gibney, P. A., Botstein, D., Storey, J. D., & Rabinowitz, J. D. (2016) · Science · doi:10.1126/science.aaf2786 · Cited by 297 · see citing works
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Christen, S., Lorendeau, D., Schmieder, R., Broekaert, D., Metzger, K., Veys, K., Elia, I., Buescher, J. M., Orth, M. F., Davidson, S. M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.042 · Cited by 258 · see citing works
Single‐Cell Mass Spectrometry Approaches to Explore Cellular Heterogeneity
Zhang, L., & Vértes, Á. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201709719 · Cited by 277 · see citing works
Mick, E., Titov, D. V., Skinner, O. S., Sharma, R., Jourdain, A. A., & Mootha, V. K. (2020) · eLife · doi:10.7554/elife.49178 · Cited by 241 · see citing works
Growth strategy of microbes on mixed carbon sources
Wang, X., Xia, K., Yang, X., & Tang, C. (2019) · Nature Communications · doi:10.1038/s41467-019-09261-3 · Cited by 242 · see citing works
Khodayari, A., & Maranas, C. D. (2016) · Nature Communications · doi:10.1038/ncomms13806 · Cited by 230 · see citing works
Christodoulou, D., Link, H., Fuhrer, T., Kochanowski, K., Gerosa, L., & Sauer, U. (2018) · Cell Systems · doi:10.1016/j.cels.2018.04.009 · Cited by 214 · see citing works
T Cell Activation Depends on Extracellular Alanine
Ron‐Harel, N., Ghergurovich, J. M., Notarangelo, G., LaFleur, M. W., Tsubosaka, Y., Sharpe, A. H., Rabinowitz, J. D., & Haigis, M. C. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.034 · Cited by 199 · see citing works
Lian, G., Gnanaprakasam, J. R., Wang, T., Wu, R., Chen, X., Liu, L., Shen, Y., Yang, M., Yang, J. J. J., Chen, Y., et al. (2018) · eLife · doi:10.7554/elife.36158 · Cited by 199 · see citing works
Affinity-based capture and identification of protein effectors of the growth regulator ppGpp
Wang, B., Dai, P., Ding, D., Rosario, A. M. D., Grant, R. A., Pentelute, B. L., & Laub, M. T. (2018) · Nature Chemical Biology · doi:10.1038/s41589-018-0183-4 · Cited by 193 · see citing works
Extraction and Quantitation of Nicotinamide Adenine Dinucleotide Redox Cofactors
Lu, W., Wang, L., Chen, L., Hui, S. T., & Rabinowitz, J. D. (2017) · Antioxidants and Redox Signaling · doi:10.1089/ars.2017.7014 · Cited by 190 · see citing works
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Mülleder, M., Calvani, E., Alam, M. T., Wang, R. K., Eckerstorfer, F., Zelezniak, A., & Ralser, M. (2016) · Cell · doi:10.1016/j.cell.2016.09.007 · Cited by 178 · see citing works
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Thornburg, Z. R., Bianchi, D. M., Brier, T. A., Gilbert, B. R., Earnest, E. E., Melo, M. C. R., Safronova, N., Sáenz, J. P., Cook, A. T., Wise, K. S., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.025 · Cited by 165 · see citing works
“While such high concentrations have been reported in yeast (Park et al., 2016), they may be inaccurate for Syn3A.”
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Olin-Sandoval, V., Yu, J. S. L., Miller-Fleming, L., Alam, M. T., Kamrad, S., Correia-Melo, C., Haas, R., Segal, J., Peña Navarro, D. A., Herrera-Dominguez, L., et al. (2019) · Nature · doi:10.1038/s41586-019-1442-6 · Cited by 163 · see citing works
Frontiers of high-throughput metabolomics
Zampieri, M., Sekar, K., Zamboni, N., & Sauer, U. (2017) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2016.12.006 · Cited by 169 · see citing works
Mitochondrial-to-nuclear communication in aging: an epigenetic perspective
Zhu, D., Li, X., & Tian, Y. (2022) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.03.008 · Cited by 165 · see citing works
Riboregulation of Enolase 1 activity controls glycolysis and embryonic stem cell differentiation
Huppertz, I., Perez-Perri, J. I., Mantas, P., Sekaran, T., Schwarzl, T., Russo, F., Ferring–Appel, D., Koskova, Z., Dimitrova-Paternoga, L., Kafkia, E., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.05.019 · Cited by 138 · see citing works
Metabolic requirements for cancer cell proliferation
Keibler, M. A., Wasylenko, T. M., Kelleher, J. K., Iliopoulos, O., Heiden, M. G. V., & Stephanopoulos, G. N. (2016) · Cancer & Metabolism · doi:10.1186/s40170-016-0156-6 · Cited by 135 · see citing works
Improving formaldehyde consumption drives methanol assimilation in engineered E. coli
Woolston, B. M., King, J. R., Reiter, M., Hove, B. V., & Stephanopoulos, G. N. (2018) · Nature Communications · doi:10.1038/s41467-018-04795-4 · Cited by 129 · see citing works
In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale
Theillet, F. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00937 · Cited by 126 · see citing works
A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations
Davidi, D., Longo, L. M., Jabłońska, J., Milo, R., & Tawfik, D. S. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00039 · Cited by 120 · see citing works
Mitochondrial ATP generation is more proteome efficient than glycolysis
Shen, Y., Dinh, H. V., Cruz, E. R., Chen, Z., Bartman, C., Xiao, T., Call, C. M., Ryseck, R.-P., Pratas, J., Weilandt, D., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01571-y · Cited by 116 · see citing works
Zou, Y., Wang, A., Huang, L. Z., Zhu, X., Hu, Q., Zhang, Y., Chen, X., Li, F., Wang, Q., Wang, H., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.02.017 · Cited by 116 · see citing works
Nutrient metabolism and cancer in the in vivo context: a metabolic game of give and take
Altea‐Manzano, P., Cuadros, A. M., Broadfield, L. A., & Fendt, S. (2020) · EMBO Reports · doi:10.15252/embr.202050635 · Cited by 119 · see citing works
Gnanaprakasam, J. R., Wu, R., & Wang, R. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01075 · Cited by 105 · see citing works
Samarah, L. Z., & Vértes, Á. (2020) · Open Research (University of Surrey) · doi:10.1002/viw.20200063 · Cited by 101 · see citing works
Gao, C., Wang, S., Hu, G., Guo, L., Chen, X., Xu, P., & Liu, L. (2017) · Biotechnology and Bioengineering · doi:10.1002/bit.26486 · Cited by 96 · see citing works
Park, Y., Pacitto, A., Bayliss, T., Cleghorn, L. A. T., Wang, Z., Hartman, T. E., Arora, K., Ioerger, T. R., Sacchettini, J. C., Rizzi, M., et al. (2016) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.6b00103 · Cited by 95 · see citing works
Pavan, M., Reinmets, K., Garg, S., Mueller, A. P., Marcellin, E., Köpke, M., & Valgepea, K. (2022) · Metabolic Engineering · doi:10.1016/j.ymben.2022.01.015 · Cited by 88 · see citing works
ppGpp Coordinates Nucleotide and Amino-Acid Synthesis in E. coli During Starvation
Wang, B., Grant, R. A., & Laub, M. T. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.08.005 · Cited by 92 · see citing works
Shimizu, K., & Matsuoka, Y. (2018) · Biotechnology Advances · doi:10.1016/j.biotechadv.2018.12.007 · Cited by 88 · see citing works
Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism
Wilkes, R. A., Waldbauer, J., Carroll, A. L., Nieto‐Domínguez, M., Parker, D. J., Zhang, L., Guss, A. M., & Aristilde, L. (2023) · Nature Chemical Biology · doi:10.1038/s41589-022-01237-7 · Cited by 91 · see citing works
Near-equilibrium glycolysis supports metabolic homeostasis and energy yield
Park, J. O., Tanner, L. B., Wei, M. H., Khana, D. B., Jacobson, T. B., Zhang, Z., Rubin, S. A., Li, H., Higgins, M. B., Stevenson, D. M., et al. (2019) · Nature Chemical Biology · doi:10.1038/s41589-019-0364-9 · Cited by 90 · see citing works
Physical basis of the cell size scaling laws
Rollin, R., Joanny, J., & Sens, P. (2023) · eLife · doi:10.7554/elife.82490 · Cited by 86 · see citing works
Donati, S., Kuntz, M. A., Pahl, V., Farke, N., Beuter, D., Glatter, T., Gomes‐Filho, J. V., Randau, L., Wang, C., & Link, H. (2020) · Cell Systems · doi:10.1016/j.cels.2020.10.011 · Cited by 88 · see citing works
Bisphosphoglycerate mutase controls serine pathway flux via 3-phosphoglycerate
Oslund, R., Su, X., Haugbro, M., Kee, J., Esposito, M., David, Y., Wang, B., Ge, E. J., Perlman, D. H., Kang, Y., et al. (2017) · Nature Chemical Biology · doi:10.1038/nchembio.2453 · Cited by 87 · see citing works
Bird, J. G., Basu, U., Kuster, D., Ramachandran, A., Grudzien‐Nogalska, E., Towheed, A., Wallace, D. C., Kiledjian, M., Temiakov, D., Patel, S. S., et al. (2018) · eLife · doi:10.7554/elife.42179 · Cited by 85 · see citing works
Temporal fluxomics reveals oscillations in TCA cycle flux throughout the mammalian cell cycle
Ahn, E., Kumar, P., Mukha, D., Tzur, A., & Shlomi, T. (2017) · Molecular Systems Biology · doi:10.15252/msb.20177763 · Cited by 85 · see citing works
Reznik, E., Christodoulou, D., Goldford, J. E., Briars, E. A., Sauer, U., Segrè, D., & Noor, Ε. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.08.066 · Cited by 80 · see citing works
Evolution of glutamatergic signaling and synapses
Moroz, L. L., Nikitin, M. A., Poličar, P. G., Kohn, A. B., & Romanova, D. Y. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108740 · Cited by 79 · see citing works
Kukurugya, M. A., Mendonca, C. M., Solhtalab, M., Wilkes, R. A., Thannhauser, T. W., & Aristilde, L. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.007885 · Cited by 78 · see citing works
In diverse conditions, intrinsic chromatin condensates have liquid-like material properties
Gibson, B. A., Blaukopf, C., Lou, T., Chen, L., Doolittle, L. K., Finkelstein, I. J., Narlikar, G. J., Gerlich, D. W., & Rosen, M. K. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218085120 · Cited by 71 · see citing works
The PEP-pyruvate-oxaloacetate node: variation at the heart of metabolism
Koendjbiharie, J. G., van Kranenburg, R., & Kengen, S. W. M. (2020) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaa061 · Cited by 74 · see citing works
Functional Roles of Chelated Magnesium Ions in RNA Folding and Function
Yamagami, R., Sieg, J. P., & Bevilacqua, P. C. (2021) · Biochemistry · doi:10.1021/acs.biochem.1c00012 · Cited by 76 · see citing works
Li, Z., Uno, N., Ding, X., Avery, L., Banach, D. B., & Liu, C. (2023) · ACS Nano · doi:10.1021/acsnano.2c12754 · Cited by 75 · see citing works
Nilsson, A., Haanstra, J. R., Engqvist, M. K. M., Gerding, A., Bakker, B. M., Klingmüller, U., Teusink, B., & Nielsen, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919250117 · Cited by 71 · see citing works
Lessons on enzyme kinetics from quantitative proteomics
Davidi, D., & Milo, R. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.02.007 · Cited by 72 · see citing works
Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets
Barupal, D. K., Fan, S., & Fiehn, O. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.01.010 · Cited by 71 · see citing works
Lee, W. D., Mukha, D., Aizenshtein, E., & Shlomi, T. (2019) · Nature Communications · doi:10.1038/s41467-019-09352-1 · Cited by 70 · see citing works
Ko, Y. J., Kim, M., You, S. K., Shin, S. K., Chang, J., Choi, H. J., Jeong, W.-Y., Lee, M.-E., Hwang, D.-H., & Han, S. O. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.04.013 · Cited by 69 · see citing works
Choudhury, S., Narayanan, B., Moret, M., Hatzimanikatis, V., & Miskovic, L. (2024) · Nature Catalysis · doi:10.1038/s41929-024-01220-6 · Cited by 67 · see citing works
Reserve Flux Capacity in the Pentose Phosphate Pathway by NADPH Binding Is Conserved across Kingdoms
Christodoulou, D., Kuehne, A., Estermann, A., Fuhrer, T., Lang, P., & Sauer, U. (2019) · iScience · doi:10.1016/j.isci.2019.08.047 · Cited by 68 · see citing works
Channeling in native microbial pathways: Implications and challenges for metabolic engineering
Abernathy, M. H., He, L., & Tang, Y. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.06.004 · Cited by 66 · see citing works
Jacobson, T. B., Adamczyk, P., Stevenson, D. M., Regner, M. R., Ralph, J., Reed, J. L., & Amador‐Noguez, D. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2019.05.006 · Cited by 63 · see citing works
Glutathione synthesis in the mouse liver supports lipid abundance through NRF2 repression
Asantewaa, G., Tuttle, E. T., Ward, N. P., Kang, Y. P., Kim, Y., Kavanagh, M. E., Girnius, N., Chen, Y., Rodriguez, K., Hecht, F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50454-2 · Cited by 63 · see citing works
Rinaldi, G., Rossi, M., & Fendt, S. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1397 · Cited by 64 · see citing works
Systematic mapping of protein‐metabolite interactions in central metabolism of Escherichia coli
Diether, M., Nikolaev, Y., Allain, F. H. ‐., & Sauer, U. (2019) · Molecular Systems Biology · doi:10.15252/msb.20199008 · Cited by 61 · see citing works
Teav, T., Gallart‐Ayala, H., van der Velpen, V., Mehl, F., Henry, H., & Ivanišević, J. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b02373 · Cited by 61 · see citing works
Genetically encoded tools for measuring and manipulating metabolism
Choe, M., & Titov, D. V. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01012-8 · Cited by 60 · see citing works
Koberstein, J. N., Stewart, M. L., Smith, C. B., Tarasov, A. I., Ashcroft, F. M., Stork, P. A., & Goodman, R. H. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204407119 · Cited by 60 · see citing works
Enzyme I facilitates reverse flux from pyruvate to phosphoenolpyruvate in Escherichia coli
Long, C. P., Au, J., Sandoval, N. R., Gebreselassie, N. A., & Antoniewicz, M. R. (2017) · Nature Communications · doi:10.1038/ncomms14316 · Cited by 60 · see citing works
Jacobson, T. B., Korosh, T. C., Stevenson, D. M., Foster, C., Maranas, C. D., Olson, D. G., Lynd, L. R., & Amador‐Noguez, D. (2020) · mSystems · doi:10.1128/msystems.00736-19 · Cited by 59 · see citing works
Buey, R. M., Fernández‐Justel, D., Marcos‐Alcalde, Í., Winter, G., Gómez‐Puertas, P., de Pereda, J. M., & Revuelta, J. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-02805-x · Cited by 58 · see citing works
Glutamine metabolism in Th17/Treg cell fate: applications in Th17 cell-associated diseases
Yang, G., Xia, Y., & Ren, W. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1703-2 · Cited by 55 · see citing works
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:10.1021/acscatal.8b04822 · Cited by 55 · see citing works
Glucose feeds the tricarboxylic acid cycle via excreted ethanol in fermenting yeast
Xiao, T., Khan, A., Shen, Y., Chen, L., & Rabinowitz, J. D. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01091-7 · Cited by 54 · see citing works
The ribosomal A-site finger is crucial for binding and activation of the stringent factor RelA
Kudrin, P., Dzhygyr, I., Ishiguro, K., Beljantseva, J., Maksimova, E. M., Oliveira, S. R. A., Varik, V., Payoe, R., Konevega, A. L., Tenson, T., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky023 · Cited by 54 · see citing works
Ahn-Horst, T. A., Mille, L. S., Sun, G., Morrison, J. H., & Covert, M. W. (2022) · npj Systems Biology and Applications · doi:10.1038/s41540-022-00242-9 · Cited by 53 · see citing works
OPA1 drives macrophage metabolism and functional commitment via p65 signaling
Sánchez‐Rodríguez, R., Tezze, C., Agnellini, A. H. R., Angioni, R., Venegas, F. C., Cioccarelli, C., Munari, F., Bertoldi, N., Canton, M., Desbats, M. A., et al. (2022) · Cell Death and Differentiation · doi:10.1038/s41418-022-01076-y · Cited by 53 · see citing works
Hepatitis C virus RNA is 5′-capped with flavin adenine dinucleotide
Sherwood, A. V., Rivera-Rangel, L. R., Ryberg, L. A., Larsen, H. S., Anker, K. M., Costa, R., Vågbø, C. B., Jakljevič, E., Pham, L. V., Fernandez-Antunez, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06301-3 · Cited by 51 · see citing works
Reversible FMN dissociation from Escherichia coli respiratory complex I
Holt, P. J., Efremov, R. G., Nakamaru‐Ogiso, E., & Sazanov, L. A. (2016) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2016.08.008 · Cited by 50 · see citing works
Thermodynamic principle to enhance enzymatic activity using the substrate affinity
Ooka, H., Chiba, Y., & Nakamura, R. (2023) · Nature Communications · doi:10.1038/s41467-023-40471-y · Cited by 48 · see citing works
Mitochondrial NAD(P) + Transhydrogenase: From Molecular Features to Physiology and Disease
Francisco, A., Figueira, T. R., & Castilho, R. F. (2021) · Antioxidants and Redox Signaling · doi:10.1089/ars.2021.0111 · Cited by 48 · see citing works
How to Recruit a Promiscuous Enzyme to Serve a New Function
Copley, S. D., Newton, M., & Widney, K. A. (2022) · Biochemistry · doi:10.1021/acs.biochem.2c00249 · Cited by 46 · see citing works
Badur, M. G., & Metallo, C. M. (2017) · Metabolic Engineering · doi:10.1016/j.ymben.2017.11.013 · Cited by 44 · see citing works
Integrating –omics data into genome-scale metabolic network models: principles and challenges
Ramon, C., Gollub, M. G., & Stelling, J. (2018) · Essays in Biochemistry · doi:10.1042/ebc20180011 · Cited by 45 · see citing works
Korte, A. R., Morris, N. J., & Vértes, Á. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.8b05074 · Cited by 45 · see citing works
Yao, C.-H., Park, J. S., Kurmi, K., Hu, S.-H., Notarangelo, G., Crowley, J., Jacobson, H., Hui, S. T., Sharpe, A. H., & Haigis, M. C. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.03.023 · Cited by 42 · see citing works
Non-Canonical G-quadruplexes cause the hCEB1 minisatellite instability in Saccharomyces cerevisiae
Piazza, A., Cui, X., Adrian, M., Samazan, F., Heddi, B., Phan, A. T., & Nicolas, A. G. (2017) · eLife · doi:10.7554/elife.26884 · Cited by 44 · see citing works
Single-Cell Mass Spectrometry of Subpopulations Selected by Fluorescence Microscopy
Zhang, L., Sevinsky, C. J., Davis, B. M., & Vértes, Á. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b05126 · Cited by 44 · see citing works
Deciphering the physiological response of Escherichia coli under high ATP demand
Boecker, S., Slaviero, G., Schramm, T., Szymanski, W. G., Steuer, R., Link, H., & Klamt, S. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110504 · Cited by 43 · see citing works
Metabolic Determinants of Cardiomyocyte Proliferation
Mohamed, T., Abouleisa, R., & Hill, B. G. (2022) · Stem Cells · doi:10.1093/stmcls/sxac016 · Cited by 41 · see citing works
Thompson, R. A., & Trinh, C. T. (2017) · Biotechnology and Bioengineering · doi:10.1002/bit.26374 · Cited by 42 · see citing works
Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast
Gonçalves, E., Nakic, Z. R., Zampieri, M., Wagih, O., Ochoa, D., Sauer, U., Beltrão, P., & Sáez-Rodríguez, J. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005297 · Cited by 42 · see citing works
Mülleder, M., Campbell, K., Matsarskaia, O., Eckerstorfer, F., & Ralser, M. (2016) · F1000Research · doi:10.12688/f1000research.9606.1 · Cited by 39 · see citing works
- 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 · 2014: 1
- 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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