Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains

Baldazzi, Valentina; Ropers, Delphine; Gouzé, Jean-Luc; Gedeon, Tomas; de Jong, Hidde

doi:10.7554/elife.79815

Cited by 12Microbial Metabolic Engineering and BioproductionGene Regulatory Network AnalysisBacterial Genetics and Biotechnology

Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains

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Baldazzi, Valentina, Ropers, Delphine, Gouzé, Jean-Luc, Gedeon, Tomas, de Jong, Hidde·2023·eLife

DOI 10.7554/elife.79815PMC PMC10270689

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Abstract

Different strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for the observed rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. An interesting prediction of our model, supported by the experimental data, is that high growth rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a more efficient utilization of proteomic resources. Our model thus contributes to a fundamental understanding of the quantitative relationship between rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology.

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Escherichia coli and Salmonella :cellular and molecular biology
Neidhardt, F. C. (1996) · ASM Press eBooks · Cited by 8,230 · see citing works
Energy charge of the adenylate pool as a regulatory parameter. Interaction with feedback modifiers
Atkinson, D. (1968) · Biochemistry · doi:10.1021/bi00851a033 · Cited by 2,417 · 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
Interdependence of Cell Growth and Gene Expression: Origins and Consequences
Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z., & Hwa, T. (2010) · Science · doi:10.1126/science.1192588 · Cited by 1,470 · see citing works
The maintenance energy of bacteria in growing cultures
Pirt, S. (1965) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1965.0069 · Cited by 1,466 · see citing works
A genome‐scale metabolic reconstruction for Escherichia coli K‐12 MG1655 that accounts for 1260 ORFs and thermodynamic information
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
Cooperation and Competition in the Evolution of ATP-Producing Pathways
Pfeiffer, T., Schuster, S., & Bonhoeffer, S. (2001) · Science · doi:10.1126/science.1058079 · Cited by 1,365 · see citing works
Energetics of bacterial growth: balance of anabolic and catabolic reactions
Russell, J. B., & Cook, G. M. (1995) · Microbiological Reviews · doi:10.1128/mr.59.1.48-62.1995 · Cited by 927 · see citing works
Modulation of Chemical Composition and Other Parameters of the Cell by Growth Rate
Bremer, H. J. (1999) · Cited by 909 · see citing works
Overflow metabolism in Escherichia coli results from efficient proteome allocation
Basan, M., Hui, S., Okano, H., Zhang, Z., Shen, Y., Williamson, J. R., & Hwa, T. (2015) · Nature · doi:10.1038/nature15765 · Cited by 785 · 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
Bacterial Metabolism
Gottschalk, G. (1986) · Springer series in microbiology · doi:10.1007/978-1-4612-1072-6 · Cited by 695 · see citing works
Shifts in growth strategies reflect tradeoffs in cellular economics
Molenaar, D., van Berlo, R., de Ridder, D., & Teusink, B. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.82 · Cited by 653 · see citing works
What is the total number of protein molecules per cell volume? A call to rethink some published values
Milo, R. (2013) · BioEssays · doi:10.1002/bies.201300066 · Cited by 643 · 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
iML1515, a knowledgebase that computes Escherichia coli traits
Monk, J. M., Lloyd, C. J., Brunk, E., Mih, N., Sastry, A. V., King, Z. A., Takeuchi, R., Nomura, W., Zhang, Z., Mori, H., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3956 · Cited by 542 · see citing works
Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471 · see citing works
Metabolite concentrations, fluxes and free energies imply efficient enzyme usage
Park, J. O., Rubin, S. A., Xu, Y.-F., Amador-Noguez, D., Fan, J., Shlomi, T., & Rabinowitz, J. D. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2077 · Cited by 453 · see citing works
Emergence of robust growth laws from optimal regulation of ribosome synthesis
Scott, M., Klumpp, S., Mateescu, E. M., & Hwa, T. (2014) · Molecular Systems Biology · doi:10.15252/msb.20145379 · Cited by 443 · see citing works
Multidimensional Optimality of Microbial Metabolism
Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M., & Sauer, U. (2012) · Science · doi:10.1126/science.1216882 · Cited by 420 · see citing works
Mechanistic links between cellular trade-offs, gene expression, and growth
Weiße, A. Y., Oyarzún, D. A., Vincent Danos, A., & Swain, P. S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1416533112 · Cited by 390 · see citing works
Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria
Hui, S., Silverman, J. M., Chen, S. S., Erickson, D. W., Basan, M., Wang, J., Hwa, T., & Williamson, J. R. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145697 · Cited by 369 · see citing works
Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity
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
Levels of major proteins of Escherichia coli during growth at different temperatures
Herendeen, S. L., VanBogelen, R. A., & Neidhardt, F. C. (1979) · Journal of Bacteriology · doi:10.1128/jb.139.1.185-194.1979 · Cited by 367 · see citing works
Studies on the role of ribonucleic acid in the growth of bacteria
Neidhardt, F. C., & Magasanik, B. (1960) · Biochimica et Biophysica Acta · doi:10.1016/0006-3002(60)90757-5 · Cited by 359 · see citing works
Microbial Maintenance: A Critical Review on Its Quantification
van Bodegom, P. M. (2007) · Microbial Ecology · doi:10.1007/s00248-006-9049-5 · Cited by 345 · see citing works
Are growth rates of Escherichia coli in batch cultures limited by respiration?
Andersen, K. B., & von Meyenburg, K. (1980) · Journal of Bacteriology · doi:10.1128/jb.144.1.114-123.1980 · Cited by 296 · see citing works
The complex relationship between microbial growth rate and yield and its implications for ecosystem processes
Lipson, D. A. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00615 · Cited by 268 · see citing works
Does microbial life always feed on negative entropy? Thermodynamic analysis of microbial growth
von Stockar, U., & Liu, J.-S. (1999) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/s0005-2728(99)00065-1 · Cited by 262 · see citing works
Flux analysis and control of the central metabolic pathways in Escherichia coli
Holms, H. (1996) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.1996.tb00255.x · Cited by 260 · see citing works
The physiology and ecological implications of efficient growth
Roller, B. R. K., & Schmidt, T. M. (2015) · The ISME Journal · doi:10.1038/ismej.2014.235 · Cited by 256 · see citing works
Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium
LaCroix, R. A., Sandberg, T. E., O'Brien, E. J., Utrilla, J., Ebrahim, A., Guzmán, G. I., Szubin, R., Palsson, B. Ø., & Feist, A. M. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.02246-14 · Cited by 254 · see citing works
Systems biology approach reveals that overflow metabolism of acetate in Escherichia coli is triggered by carbon catabolite repression of acetyl-CoA synthetase
Valgepea, K., Adamberg, K., Nahku, R., Lahtvee, P., Arike, L., & Vilu, R. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-166 · Cited by 247 · see citing works
Phylogenetic Diversity in the Macromolecular Composition of Microalgae
Finkel, Z. V., Follows, M. J., Liefer, J. D., Brown, C. M., Benner, I., & Irwin, A. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155977 · Cited by 246 · see citing works
A global resource allocation strategy governs growth transition kinetics of Escherichia coli
Erickson, D. W., Schink, S. J., Patsalo, V., Williamson, J. R., Gerland, U., & Terence Hwa, B. (2017) · Nature · doi:10.1038/nature24299 · Cited by 239 · see citing works
How fast‐growing bacteria robustly tune their ribosome concentration to approximate growth‐rate maximization
Bosdriesz, E., Douwe Molenaar, B., Teusink, B., & Bruggeman, F. J. (2015) · FEBS Journal · doi:10.1111/febs.13258 · Cited by 229 · see citing works
Growth rate of polypeptide chains as a function of the cell growth rate in a mutant of Escherichia coli 15
Forchhammer, J., & Lindahl, L. (1971) · Journal of Molecular Biology · doi:10.1016/0022-2836(71)90337-8 · Cited by 221 · see citing works
Principles of cellular resource allocation revealed by condition-dependent proteome profiling
Metzl-Raz, E., Kafri, M., Yaakov, G., Soifer, I., Gurvich, Y., & Barkai, N. (2017) · eLife · doi:10.7554/elife.28034 · Cited by 221 · see citing works
Inflating bacterial cells by increased protein synthesis
Basan, M., Zhu, M., Dai, X., Warren, M., Sévin, D. C., Wang, Y., & Terence Hwa, B. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156178 · Cited by 208 · see citing works
Economics of membrane occupancy and respiro‐fermentation
Zhuang, K. H., Vemuri, G. N., & Mahadevan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.34 · Cited by 205 · see citing works
Why Do Fast-Growing Bacteria Enter Overflow Metabolism? Testing the Membrane Real Estate Hypothesis
Szenk, M., Dill, K. A., & de Graff, A. M. R. (2017) · Cell Systems · doi:10.1016/j.cels.2017.06.005 · Cited by 203 · see citing works
Functioning of a metabolic flux sensor in Escherichia coli
Kochanowski, K., Volkmer, B., Gerosa, L., Haverkorn van Rijsewijk, B. R., Schmidt, A., & Heinemann, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202582110 · Cited by 196 · see citing works
Relationship between Growth Rate and ATP Concentration in Escherichia coli
Schneider, D. A., & Gourse, R. L. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m311996200 · Cited by 190 · see citing works
The Allosteric Regulation of Pyruvate Kinase
Valentini, G., Chiarelli, L. R., Fortin, R., Speranza, M. L., Galizzi, A., & Mattevi, A. (2000) · Journal of Biological Chemistry · doi:10.1074/jbc.m001870200 · Cited by 190 · see citing works
Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters
Adadi, R., Volkmer, B., Milo, R., Heinemann, M., & Shlomi, T. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002575 · Cited by 179 · see citing works
Metabolic costs of amino acid and protein production in Escherichia coli
Kaleta, C., Schäuble, S., Rinas, U., & Schuster, S. (2013) · Biotechnology Journal · doi:10.1002/biot.201200267 · Cited by 177 · see citing works
Constrained Allocation Flux Balance Analysis
Mori, M., Terence Hwa, B., Martin, O., Martino, A. D., & Marinari, E. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004913 · Cited by 169 · see citing works
Bacterial growth laws reflect the evolutionary importance of energy efficiency
Maitra, A., & Dill, K. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421138111 · Cited by 162 · see citing works
Large‐scale 13C‐flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli
van Rijsewijk, B. R. B. H., Nanchen, A., Nallet, S., Kleijn, R. J., & Sauer, U. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.9 · Cited by 160 · see citing works
Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity
Fendt, S., Buescher, J. M., Rudroff, F., Picotti, P., Zamboni, N., & Sauer, U. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.11 · Cited by 159 · see citing works
Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data
Gerosa, L., van Rijsewijk, B. R. B. H., Christodoulou, D., Kochanowski, K., Schmidt, T., Noor, Ε., & Sauer, U. (2015) · Cell Systems · doi:10.1016/j.cels.2015.09.008 · Cited by 157 · see citing works
Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes
Monk, J. M., Koza, A., Campodonico, M. A., Machado, D., Seoane, J. M., Palsson, B. Ø., Herrgård, M. J., & Feist, A. M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.013 · Cited by 147 · see citing works
Escherichia coli achieves faster growth by increasing catalytic and translation rates of proteins
Valgepea, K., Adamberg, K., Seiman, A., & Vilu, R. (2013) · Molecular BioSystems · doi:10.1039/c3mb70119k · Cited by 145 · see citing works
Proteome reallocation in Escherichia coli 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 · see citing works
Nonlinear Dependency of Intracellular Fluxes on Growth Rate in Miniaturized Continuous Cultures of Escherichia coli
Nanchen, A., Schicker, A., & Sauer, U. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.72.2.1164-1172.2006 · Cited by 140 · see citing works
Optimality and sub-optimality in a bacterial growth law
Towbin, B. D., Kohanim, Y. K., Bren, A., Doron, S., Sorek, R., & Alon, U. S. (2017) · Nature Communications · doi:10.1038/ncomms14123 · Cited by 129 · see citing works
Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield
Wortel, M. T., Noor, Ε., Ferris, M. C., Bruggeman, F. J., & Liebermeister, W. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006010 · Cited by 118 · see citing works
Metabolic trade-offs and the maintenance of the fittest and the flattest
Beardmore, R., Gudelj, I., Lipson, D. A., & Hurst, L. D. (2011) · Nature · doi:10.1038/nature09905 · Cited by 115 · see citing works
Quantitative insights into the cyanobacterial cell economy
Zavřel, T., Faizi, M., Loureiro, C., Poschmann, G., Stühler, K., Sinetova, M. A., Zorina, A. A., Steuer, R., & Červený, J. (2019) · eLife · doi:10.7554/elife.42508 · Cited by 114 · see citing works
Dynamical Allocation of Cellular Resources as an Optimal Control Problem: Novel Insights into Microbial Growth Strategies
Giordano, N., Mairet, F., Gouzé, J., Geiselmann, J., & de Jong, H. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004802 · Cited by 113 · see citing works
Self-citation
Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments
O’Brien, E. J., Utrilla, J., & Palsson, B. Ø. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004998 · Cited by 113 · see citing works
Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth
Reimers, A.-M., Knoop, H., Bockmayr, A., & Steuer, R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617508114 · Cited by 113 · see citing works
Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution
Utrilla, J., O’Brien, E. J., Chen, K., McCloskey, D., Cheung, J., Wang, H. H., Armenta‐Medina, D., Feist, A. M., & Palsson, B. Ø. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.003 · Cited by 112 · see citing works
Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol
Cheng, K., Lee, B.-S., Masuda, T., Ito, T., Ikeda, K., Hirayama, A., Deng, L., Dong, J., Shimizu, K., Soga, T., et al. (2014) · Nature Communications · doi:10.1038/ncomms4233 · Cited by 111 · see citing works
Metabolic regulation is sufficient for global and robust coordination of glucose uptake, catabolism, energy production and growth in Escherichia coli
Millard, P., B, K. S. A., & Mendes, P. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005396 · Cited by 108 · see citing works
Searching for principles of microbial physiology
Bruggeman, F. J., Planqué, R., Douwe Molenaar, B., & Teusink, B. (2020) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaa034 · Cited by 104 · see citing works
The Energetics of Escherichia coli during Aerobic Growth in Continuous Culture
Farmer, I. S., & Jones, C. W. (1976) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.1976.tb10639.x · Cited by 93 · see citing works
Dual role of transcription and transcript stability in the regulation of gene expression inEscherichia colicells cultured on glucose at different growth rates
Esquerré, T., Laguerre, S., Turlan, C., Carpousis, A. J., Girbal, L., & Cocaign‐Bousquet, M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1150 · Cited by 87 · see citing works
An analytical theory of balanced cellular growth
Dourado, H., & Lercher, M. J. (2020) · Nature Communications · doi:10.1038/s41467-020-14751-w · Cited by 79 · see citing works
A yield-cost tradeoff governs Escherichia coli’s decision between fermentation and respiration in carbon-limited growth
Mori, M., Marinari, E., & Martino, A. D. (2019) · npj Systems Biology and Applications · doi:10.1038/s41540-019-0093-4 · Cited by 73 · 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
Invariance of the Nucleoside Triphosphate Pools ofEscherichia coli with Growth Rate
Petersen, C., & Møller, L. B. (2000) · Journal of Biological Chemistry · doi:10.1074/jbc.275.6.3931 · Cited by 70 · see citing works
Mathematical modelling of microbes: metabolism, gene expression and growth
de Jong, H., Casagranda, S., Giordano, N., Eugenio Cinquemani, B., Ropers, D., Geiselmann, J., & Gouzé, J. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0502 · Cited by 60 · see citing works
Self-citation
Analysis of global control of Escherichia coli carbohydrate uptake
Kremling, A., Bettenbrock, K., & Gilles, E. D. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-42 · Cited by 51 · see citing works
Physiological, biomass elemental composition and proteomic analyses of Escherichia coli ammonium-limited chemostat growth, and comparison with iron- and glucose-limited chemostat growth
Folsom, J. P., & Carlson, R. P. (2015) · Microbiology · doi:10.1099/mic.0.000118 · Cited by 51 · see citing works
Rethinking cell growth models
Kafri, M., Metzl-Raz, E., Jonas, F., & Barkai, N. (2016) · FEMS Yeast Research · doi:10.1093/femsyr/fow081 · Cited by 48 · see citing works
Complete Genome Sequence of Escherichia coli NCM3722
Brown, S. D., & Jun, S. (2015) · Genome Announcements · doi:10.1128/genomea.00879-15 · Cited by 46 · see citing works
The post‐transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of Escherichia coli
Morin, M., Ropers, D., Létisse, F., Laguerre, S., Portais, J., Cocaign‐Bousquet, M., & Enjalbert, B. (2016) · Molecular Microbiology · doi:10.1111/mmi.13343 · Cited by 46 · see citing works
Self-citation
Optimal proteome allocation and the temperature dependence of microbial growth laws
Mairet, F., Gouzé, J., & de Jong, H. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00172-y · Cited by 44 · see citing works
Self-citation
On the optimality of the enzyme–substrate relationship in bacteria
Dourado, H., Mori, M., Terence Hwa, B., & Lercher, M. J. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001416 · Cited by 40 · see citing works
Microbe
Schaechter, M., Ingraham, J. L., & Neidhardt, F. C. (2006) · doi:10.1128/9781555813208 · Cited by 38 · see citing works
Chemical composition of selected marine microalgae, with emphasis on lipid and carbohydrate production for potential use as feed resources
Reitan, K. I., Øie, G., Jørgensen, H. Y., & Wang, X. (2021) · Journal of Applied Phycology · doi:10.1007/s10811-021-02586-x · Cited by 37 · see citing works
Elementary Growth Modes provide a molecular description of cellular self-fabrication
de Groot, D. H., Hulshof, J., Teusink, B., Bruggeman, F. J., & Planqué, R. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007559 · Cited by 35 · see citing works
Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism
Cheng, C., O’Brien, E. J., McCloskey, D., Utrilla, J., Olson, C. A., LaCroix, R. A., Sandberg, T. E., Feist, A. M., Palsson, B. Ø., & King, Z. A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007066 · Cited by 34 · see citing works
Optimal control of bacterial growth for the maximization of metabolite production
Yegorov, I., Mairet, F., de Jong, H., & Gouzé, J. (2018) · Journal of Mathematical Biology · doi:10.1007/s00285-018-1299-6 · Cited by 33 · see citing works
Self-citation
Crosstalk between transcription and metabolism: how much enzyme is enough for a cell?
Donati, S., Sander, T., & Link, H. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1396 · Cited by 30 · see citing works
RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli
McCloskey, D., Xu, J. Z., Schrübbers, L. C., Christensen, H. B., & Herrgård, M. J. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.04.009 · Cited by 30 · see citing works
A model of optimal protein allocation during phototrophic growth
Faizi, M., Zavřel, T., Loureiro, C., Červený, J., & Steuer, R. (2018) · Biosystems · doi:10.1016/j.biosystems.2018.02.004 · Cited by 26 · see citing works

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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 · see citing works
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Bruinsma, L. (2023) · doi:10.18174/590703
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Perez, L., & Cremer, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.22.666192
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Dourado, H., & Lercher, M. J. (2026) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2026.100602
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Leung, C. F. A., & Kolomeisky, A. B. (2026) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c04333
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Subramanian, P., Modankap, S. H., Ali, S. B., Baylon, J. L. D., Dhingra, L., Ali, N. M., & Ling, M. H. (2026) · Journal of Clinical Immunology & Microbiology · doi:10.46889/jcim.2026.7303
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Putnam, C., Wilken, S. E., Beck, A. E., Beyß, M., Hunt, K., Ebenhöh, O., Nöh, K., & Carlson, R. P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.13.758985

Authors: Baldazzi, V., Ropers, D., Gouzé, J.-L., Gedeon, T., & de Jong, H.
Affiliations: Centre National de la Recherche Scientifique (FR)
Institut national de recherche en sciences et technologies du numérique (FR)
Montana State University
Université Grenoble Alpes, Inria
Corresponding author: Baldazzi, Valentina
ORCID: Baldazzi, Valentina
Ropers, Delphine
Gouzé, Jean-Luc
de Jong, Hidde
Venue: eLife
Publisher: eLife Sciences Publications, Ltd
Published: 2023-05-31
Volume: 12
Pages: 1-29
ISSN: 2050-084X (ISSN-L)
DOI: 10.7554/elife.79815
PMC: PMC10270689
OpenAlex: W4378783540
Cited by: 12
References: 88
Citations by year: 2026: 5 · 2025: 3 · 2024: 3 · 2023: 1
FWCI: 1.07
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
creativecommons.org/licenses/by/4.0/ (am)
creativecommons.org/licenses/by/4.0/ (tdm)
Locations: eLife (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Montana State University ScholarWorks (Montana State University) (submittedVersion, cc-by)
HAL (submittedVersion, cc-by)
HAL (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: French National Research Agency (Maximic project (ANR-17-CE40-0024))
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 › Genetics › Bacterial Genetics and Biotechnology
Keywords: resource allocation, Escherichia coli, bacterial growth rate, growth yield, proteome composition, coarse-grained models, glucose uptake, enzyme saturation, strain differences, metabolic engineering
MeSH terms: Escherichia coli, Ribosomes, Resource Allocation, Proteomics, Metabolic Engineering

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Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains

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