Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k cat measurements
Open accessAbstract
Significance: The k cat values of enzymes are important for the study of metabolic systems. However, the current use of k cat presents major difficulties, as values for most enzymes have not been experimentally measured, and experimentally available values are often measured under nonphysiological conditions, thereby casting doubt on the relevance of k cat under in vivo conditions. We present an approach that utilizes omics data to quantitatively analyze the relationship between in vitro k cat values and the maximal catalytic rate of enzymes in vivo. Our approach offers a high-throughput method to obtain enzyme kinetic constants, which reflect in vivo conditions, and are useful for more accurate and complete cellular metabolic models.
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
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?
There is a good correlation between in vitro kcat measurements from BRENDA and in vivo values inferred from omics data.
“Although it has recently been shown that there is a good correlation between in vitro k cat measurements from BRENDA and in vivo values inferred from omics data (Davidi et al, 2016), the future use of this model for specific purposes such as flux prediction of a given pathway should always involve a preliminary manual curation of the k cat values of the respective pathway.”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Davidi et al, 2016
In vitro kcat is known for only about 10% of all enzyme-catalyzed reactions in E. coli.
“even for Escherichia coli , arguably the biochemically best-characterized organism, in vitro k cat is known for only ~10% of all enzyme-catalyzed reactions 11 .”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [11]
Davidi et al.'s approach is limited to very-well studied organisms, and even for E. coli, kcat values for only 436 enzymes could be estimated in this way.
“However, this approach is limited to very-well studied organisms, and even for E. coli , k cat values for only 436 enzymes could be estimated in this way.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [11]
Davidi et al.'s approach leads to kcat estimates that are highly correlated with experimentally measured values.
“Their approach leads to k cat estimates that are highly correlated with experimentally measured values”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Genetic regulatory mechanisms in the synthesis of proteins
Jacob, F., & Monod, J. (1961) · Journal of Molecular Biology · doi:10.1016/s0022-2836(61)80072-7 · Cited by 6,605 · see citing works
Escherichia Coli and Salmonella: Typhimurium Cellular and Molecular Biology
Neidhardt, F. C., & Ingraham, J. L. (1987) · Medical Entomology and Zoology · Cited by 4,089 · see citing works
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903 · 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
COBRApy: COnstraints-Based Reconstruction and Analysis for Python
Ebrahim, A., Lerman, J. A., Palsson, B. O., & Hyduke, D. R. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-74 · Cited by 1,378 · see citing works
A Whole-Cell Computational Model Predicts Phenotype from Genotype
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., & Covert, M. W. (2012) · Cell · doi:10.1016/j.cell.2012.05.044 · Cited by 1,300 · see citing works
The effects of alternate optimal solutions in constraint-based genome-scale metabolic models
Mahadevan, R., & Schilling, C. H. (2003) · Metabolic Engineering · doi:10.1016/j.ymben.2003.09.002 · Cited by 1,203 · see citing works
BioNumbers—the database of key numbers in molecular and cell biology
Milo, R., Jorgensen, P., Moran, U., Weber, G., & Springer, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp889 · Cited by 1,055 · see citing works
Self-citationThe Moderately Efficient Enzyme: Evolutionary and Physicochemical Trends Shaping Enzyme Parameters
Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D. S., & Milo, R. (2011) · Biochemistry · doi:10.1021/bi2002289 · Cited by 1,050 · see citing works
Self-citationA comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011
Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., & Palsson, B. Ø. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.65 · Cited by 1,006 · see citing works
BRENDA, the enzyme database: updates and major new developments
Schomburg, I. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh081 · Cited by 952 · see citing works
Edwards, J. S., Ibarra, R. U., & Palsson, B. Ø. (2001) · Nature Biotechnology · doi:10.1038/84379 · Cited by 941 · see citing works
The depth of chemical time and the power of enzymes as catalysts.
Wolfenden, R., & Snider, M. (2001) · Accounts of Chemical Research · doi:10.1021/ar000058i · Cited by 823 · see citing works
Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models
Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Adkins, J., Schramm, G., Purvine, S. O., López‐Ferrer, D., et al. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.47 · Cited by 757 · see citing works
The quantitative and condition-dependent Escherichia coli proteome
Schmidt, A., Kochanowski, K., Vedelaar, S. R., Ahrné, E., Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold, R. H., & Heinemann, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3418 · Cited by 737 · see citing works
Multiple High-Throughput Analyses Monitor the Response of E. coli to Perturbations
Ishii, N., Nakahigashi, K., Baba, T., Robert, M., Soga, T., Kanai, A., Hirasawa, T., Naba, M., Hirai, K., Hoque, A., et al. (2007) · Science · doi:10.1126/science.1132067 · Cited by 668 · see citing works
Allosteric regulation and catalysis emerge via a common route
Goodey, N. M., & Benkovic, S. J. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.98 · Cited by 667 · see citing works
Coordination of microbial metabolism
Chubukov, V., Gerosa, L., Kochanowski, K., & Sauer, U. (2014) · Nature Reviews Microbiology · doi:10.1038/nrmicro3238 · Cited by 606 · see citing works
EcoCyc: fusing model organism databases with systems biology
Keseler, I. M., Mackie, A. M., Peralta-Gil, M., Santos-Zavaleta, A., Gama‐Castro, S., Bonavides-Martínez, C., Fulcher, C. A., Huerta, A. M., Kothari, A., Krummenacker, M., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1027 · Cited by 542 · 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
Self-citationDesign and analysis of synthetic carbon fixation pathways
Bar‐Even, A., Noor, Ε., Lewis, N. E., & Milo, R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907176107 · Cited by 509 · see citing works
Self-citationComputational protein design enables a novel one-carbon assimilation pathway
Siegel, J. B., Smith, A. L., Poust, S. K., Wargacki, A. J., Bar‐Even, A., Louw, C., Shen, B. W., Eiben, C. B., Tran, H. M., Noor, Ε., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1500545112 · Cited by 409 · see citing works
Self-citationVisual 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
Self-citationMachado, D., & Herrgård, M. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003580 · Cited by 403 · see citing works
Sauer, U., Lasko, D. R., Fiaux, J., Hochuli, M., Glaser, R. W., Szyperski, T., Kurt Wüthrich, A., & Bailey, J. E. (1999) · Journal of Bacteriology · doi:10.1128/jb.181.21.6679-6688.1999 · Cited by 371 · see citing works
An overview of enzymatic reagents for the removal of affinity tags
Waugh, D. S. (2011) · Protein Expression and Purification · doi:10.1016/j.pep.2011.08.005 · Cited by 359 · see citing works
Pathway Thermodynamics Highlights Kinetic Obstacles in Central Metabolism
Noor, Ε., Bar‐Even, A., Flamholz, A. I., Reznik, E., Liebermeister, W., & Milo, R. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003483 · Cited by 342 · see citing works
Self-citationKlipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H. R., & Herwig, R. (2009) · Cited by 308 · see citing works
Self-citationPhysical limits of cells and proteomes
Dill, K. A., Ghosh, K., & Schmit, J. D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114477108 · Cited by 298 · see citing works
Holzhütter, H. (2004) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.2004.04213.x · Cited by 295 · 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
Self-citationFat synthesis in adipose tissue. An examination of stoichiometric constraints
Fell, D. A., & SMALL, J. R. (1986) · Biochemical Journal · doi:10.1042/bj2380781 · Cited by 276 · see citing works
Shlomi, T., Benyamini, T., Gottlieb, E., Sharan, R., & Ruppin, E. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002018 · Cited by 244 · see citing works
Self-citationChubukov, V., Uhr, M., Le Chat, L., Kleijn, R. J., Jules, M., Link, H., Aymerich, S., Stelling, J., & Sauer, U. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.66 · Cited by 191 · see citing works
Thermodynamics of enzyme-catalyzed reactions--a database for quantitative biochemistry
Goldberg, R. N., Tewari, Y. B., & Bhat, T. N. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth314 · Cited by 191 · see citing works
The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes
Furnham, N., Holliday, G. L., de Beer, T., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1243 · Cited by 181 · 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
Accelerating the reconstruction of genome-scale metabolic networks
Notebaart, R. A., van Enckevort, F. H. J., Francke, C., Siezen, R. J., & Teusink, B. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-296 · Cited by 157 · see citing works
Determination of bacterial cell volume with the Coulter Counter
KUBITSCHEK, H. E., & Friske, J. A. (1986) · Journal of Bacteriology · doi:10.1128/jb.168.3.1466-1467.1986 · Cited by 148 · see citing works
A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes
Smallbone, K., Messiha, H. L., Carroll, K. M., Winder, C. L., Malys, N., Dunn, W. B., Murabito, E., Swainston, N., Dada, J. O., Khan, F., et al. (2013) · FEBS Letters · doi:10.1016/j.febslet.2013.06.043 · Cited by 147 · see citing works
Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation
Liebermeister, W., Uhlendorf, J., & Klipp, E. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq141 · Cited by 146 · see citing works
Self-citationValgepea, K., Adamberg, K., Seiman, A., & Vilu, R. (2013) · Molecular BioSystems · doi:10.1039/c3mb70119k · Cited by 145 · see citing works
Proteome reallocation in <i>Escherichia coli</i> with increasing specific growth rate
Peebo, K., Valgepea, K., Maser, A., Nahku, R., Adamberg, K., & Vilu, R. (2015) · Molecular BioSystems · doi:10.1039/c4mb00721b · Cited by 145 · cited 2× · see citing works
“(25) data were reported in copies per fL cytoplasm, given condition specific cell size measurements.”
BackgroundQuantitative prediction of genome-wide resource allocation in bacteria
Goelzer, A., Muntel, J., Chubukov, V., Jules, M., Prestel, E., Nölker, R., Mariadassou, M., Aymerich, S., Hecker, M., Noirot, P., et al. (2015) · Metabolic Engineering · doi:10.1016/j.ymben.2015.10.003 · Cited by 140 · see citing works
Zhao, G., & Winkler, M. E. (1995) · Journal of Bacteriology · doi:10.1128/jb.177.4.883-891.1995 · Cited by 127 · see citing works
A note on the kinetics of enzyme action: A decomposition that highlights thermodynamic effects
Noor, Ε., Flamholz, A. I., Liebermeister, W., Bar‐Even, A., & Milo, R. (2013) · FEBS Letters · doi:10.1016/j.febslet.2013.07.028 · Cited by 123 · see citing works
Self-citationUntangling the glutamate dehydrogenase allosteric nightmare
Smith, T. J., & Stanley, C. A. (2008) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2008.07.007 · Cited by 106 · see citing works
Labhsetwar, P., Cole, J. A., Roberts, E., Price, N. D., & Luthey-Schulten, Z. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222569110 · Cited by 94 · see citing works
Kim, J., & Reed, J. L. (2012) · Genome biology · doi:10.1186/gb-2012-13-9-r78 · Cited by 89 · see citing works
García‐Contreras, R., Vos, P. F., Westerhoff, H. V., & Boogerd, F. C. (2012) · FEBS Journal · doi:10.1111/febs.12007 · Cited by 86 · see citing works
Biochemical thermodynamics: applications of Mathematica.
Alberty, R. A. (2006) · PubMed · Cited by 85 · see citing works
Quantification and Mass Isotopomer Profiling of α-Keto Acids in Central Carbon Metabolism
Zimmermann, M., Sauer, U., & Zamboni, N. (2014) · Analytical Chemistry · doi:10.1021/ac500472c · Cited by 84 · see citing works
Metabolic regulation: A control analytic perspective
Hofmeyr, J. S. (1995) · Journal of Bioenergetics and Biomembranes · doi:10.1007/bf02110188 · Cited by 67 · see citing works
Singh, V. K., & Ghosh, I. (2006) · Theoretical Biology and Medical Modelling · doi:10.1186/1742-4682-3-27 · Cited by 65 · see citing works
Biochemical networks with uncertain parameters
Liebermeister, W., & Klipp, E. (2005) · Systems Biology · doi:10.1049/ip-syb:20045033 · Cited by 63 · see citing works
Self-citationDrewke, C., Klein, M., Clade, D., et al. (1996) · FEBS Letters · doi:10.1016/0014-5793(96)00652-7 · Cited by 61 · see citing works
Inglese, J., Johnson, D. L., Benkovic, S. J., et al. (1990) · Biochemistry · doi:10.1021/bi00458a014 · Cited by 59 · see citing works
Applications of metabolic modelling to plant metabolism
Poolman, M. G., Assmus, H. E., & Fell, D. A. (2004) · Journal of Experimental Botany · doi:10.1093/jxb/erh090 · Cited by 59 · see citing works
Goel, A., dos Santos, F. B., de Vos, W. M., Teusink, B., & Douwe Molenaar, B. (2011) · Applied and Environmental Microbiology · doi:10.1128/aem.05276-11 · Cited by 58 · see citing works
Data-driven integration of genome-scale regulatory and metabolic network models
Imam, S., Schäuble, S., Brooks, A. N., Baliga, N. S., & Price, N. D. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00409 · Cited by 56 · see citing works
Parameter Balancing in Kinetic Models of Cell Metabolism
Lubitz, T., Schulz, M., Klipp, E., & Liebermeister, W. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp108764b · Cited by 52 · see citing works
Self-citationPosttranslational regulation of microbial metabolism
Kochanowski, K., Sauer, U., & Noor, Ε. (2015) · Current Opinion in Microbiology · doi:10.1016/j.mib.2015.05.007 · Cited by 51 · see citing works
Buoyant density of Escherichia coli is determined solely by the osmolarity of the culture medium
Baldwin, W. W., Myer, R., Anderson, E., et al. (1995) · Archives of Microbiology · doi:10.1007/s002030050248 · Cited by 43 · see citing works
Gautam, A., Rishi, P., & Tewari, R. (2011) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-011-3512-z · Cited by 40 · see citing works
Kinetic and Thermodynamic Aspects of Enzyme Control and Regulation
Rohwer, J. M., & Hofmeyr, J. S. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp108412s · Cited by 33 · see citing works
Crouvoisier, M., Auger, G., Blanot, D., et al. (2007) · Biochimie · doi:10.1016/j.biochi.2007.06.011 · Cited by 28 · see citing works
Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria
Eckdahl, T. T., Campbell, A. M., Heyer, L. J., Poet, J. L., Blauch, D. N., Snyder, N. L., Atchley, D. T., Baker, E. J., Brown, M., Brunner, E. C., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118322 · Cited by 28 · see citing works
A Further Note on the Kinetics of Enzyme Action
Briggs, G. E. (1925) · Biochemical Journal · doi:10.1042/bj0191037 · Cited by 24 · see citing works
Schauer, S., Lüer, C., & Moser, J. (2003) · Protein Expression and Purification · doi:10.1016/s1046-5928(03)00184-0 · Cited by 23 · see citing works
Gráczer, É., Varga, A., Melnik, B. S., et al. (2009) · Biochemistry · doi:10.1021/bi801857t · Cited by 9 · see citing works
Escherichia coli and Salmonella typhimurium; Cellular and Molecular Biology, Vol. 1 (of2)
Booth, I. R. (1988) · Trends in Biochemical Sciences · doi:10.1016/0968-0004(88)90241-1 · Cited by 8 · see citing works
Fundamentals of enzyme kinetics (Cornish-Bowden, Athel)
Boeker, E. A. (1981) · Journal of Chemical Education · doi:10.1021/ed058pa114.3 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Davidi et al. PNAS | March 22, 2016 | vol. 113 | no. 12 | 3405 SYSTEMS BIOLOGY
Cited by
Sorted by citations
Sánchez, B. J., Zhang, C., Nilsson, A., Lahtvee, P., Kerkhoven, E. J., & Nielsen, J. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167411 · Cited by 466 · see citing works
Heckmann, D., Lloyd, C. J., Mih, N., Ha, Y., Zielinski, D. C., Haiman, Z. B., Desouki, A. A., Lercher, M. J., & Palsson, B. O. (2018) · Nature Communications · doi:10.1038/s41467-018-07652-6 · Cited by 218 · see citing works
Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning
Kroll, A., Rousset, Y., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-39840-4 · Cited by 186 · see citing works
Energy metabolism controls phenotypes by protein efficiency and allocation
Chen, Y., & Nielsen, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906569116 · Cited by 156 · see citing works
Deep learning allows genome-scale prediction of Michaelis constants from structural features
Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001402 · Cited by 132 · see citing works
Biological insights through omics data integration
Noor, E., Cherkaoui, S., & Sauer, U. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.03.007 · Cited by 83 · see citing works
Self-citationEnzyme capacity-based genome scale modelling of CHO cells
Yeo, H. C., Hong, J., Lakshmanan, M., & Lee, D.-Y. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.04.005 · Cited by 75 · 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
Self-citationPopulation FBA predicts metabolic phenotypes in yeast
Labhsetwar, P., Melo, M. C. R., Cole, J. A., & Luthey-Schulten, Z. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005728 · Cited by 21 · see citing works
Data integration across conditions improves turnover number estimates and metabolic predictions
Wendering, P., Arend, M., Razaghi-Moghadam, Z., & Nikoloski, Z. (2023) · Nature Communications · doi:10.1038/s41467-023-37151-2 · Cited by 23 · see citing works
Upgrading a cyanobacterial genome-scale model by inclusion of enzymatic constraints
Andrews, F., Scrutton, N. S., & Schwartz, J.-M. (2024) · Algal Research · doi:10.1016/j.algal.2024.103584 · Cited by 7 · see citing works
- Authors
- Davidi, D., Noor, E., Liebermeister, W., Bar-Even, A., Flamholz, A., Tummler, K., Barenholz, U., Goldenfeld, M., Shlomi, T., & Milo, R.
- Affiliations
- Weizmann Institute of Science (IL)
- Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland;
- Charité – Universitätsmedizin Berlin (DE)
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany;
- University of California, Berkeley (US)
- Humboldt-Universität zu Berlin (DE)
- Technion – Israel Institute of Technology (IL)
- Corresponding author
- Milo, Ron
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2016-03-07
- Volume
- 113
- Issue
- 12
- Pages
- 3401-3406
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4812741
- OpenAlex
- W2293953879
- Cited by
- 266
- References
- 72
- Citations by year
- 2026: 29 · 2025: 28 · 2024: 38 · 2023: 33 · 2022: 16 · 2021: 28 · 2020: 30 · 2019: 21 · 2018: 18 · 2017: 17 · 2016: 7 · 2015: 1
- FWCI
- 10.92
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- EC | European Research Council (SYMPAC 260392)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- enzyme turnover number, turnover number, enzyme kinetics, enzyme saturation, thermodynamics, Escherichia coli, omics data, in vitro-in vivo correlation
- MeSH terms
- Catalysis, Enzymes
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
