A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes
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We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae . Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom‐up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.
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kcat can vary by orders of magnitude even between isoenzymes that catalyze the same reaction but differ in amino acid sequence.
“It can vary by orders of magnitude even between isoenzymes that catalyze the same reaction but differ in amino acid sequence 28 .”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [28]
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Global analysis of protein expression in yeast
Ghaemmaghami, S., Huh, W.-K., Bower, K., Howson, R. W., Belle, A., Dephoure, N., O'Shea, E. K., & Weissman, J. S. (2003) · Nature · doi:10.1038/nature02046 · Cited by 3,750 · see citing works
Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg015 · Cited by 3,060 · see citing works
Self-citationCOPASI—a COmplex PAthway SImulator
Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., & Kummer, U. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl485 · Cited by 2,618 · see citing works
Self-citationMolecular evidence for an ancient duplication of the entire yeast genome
Wolfe, K. H., & Shields, D. C. (1997) · Nature · doi:10.1038/42711 · Cited by 1,785 · see citing works
Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae
Kellis, M., Birren, B. W., & Lander, E. S. (2004) · Nature · doi:10.1038/nature02424 · Cited by 1,500 · see citing works
Minton, A. P. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.r100005200 · Cited by 1,474 · cited 1× · see citing works
“This could be explained by macromolecular crowding [81], which has the effect that the space available for water and dissolved molecules is much smaller than the total space, and so the effective volume is lower.”
BackgroundKACSER, H., & Ja, B. (1973) · PubMed · Cited by 1,452 · 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 MOLECULAR BASIS OF DOMINANCE
KACSER, H., & Burns, J. A. (1981) · Genetics · doi:10.1093/genetics/97.3-4.639 · Cited by 1,040 · see citing works
A community-driven global reconstruction of human metabolism
Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2488 · Cited by 1,019 · see citing works
Self-citationZaldivar, J., Nielsen, J., & Lisbeth Olsson, A. (2001) · Applied Microbiology and Biotechnology · doi:10.1007/s002530100624 · Cited by 953 · see citing works
Davey, H. M., & Kell, D. B. (1996) · Microbiological Reviews · doi:10.1128/mr.60.4.641-696.1996 · Cited by 879 · see citing works
Self-citationMultifunctional α-enolase: its role in diseases
Pancholi, V. (2001) · Cellular and Molecular Life Sciences · doi:10.1007/pl00000910 · Cited by 839 · see citing works
Zelena, E., Dunn, W. B., Broadhurst, D., Francis‐McIntyre, S., Carroll, K. M., Begley, P., O’Hagan, S., Knowles, J. D., Halsall, A., HUSERMET Consortium, et al. (2009) · Analytical Chemistry · doi:10.1021/ac8019366 · Cited by 815 · see citing works
Self-citationTeusink, B., Passarge, J., Reijenga, C. A., Esgalhado, E., van der Weijden, C. C., Schepper, M., Walsh, M. C., Bakker, B. M., van Dam, K., Westerhoff, H. V., et al. (2000) · European Journal of Biochemistry · doi:10.1046/j.1432-1327.2000.01527.x · Cited by 694 · see citing works
Self-citationSystems biology properties of reconstructed networks
Palsson, B. Ø. (2006) · Cited by 684 · see citing works
Mapping Pathways and Phenotypes by Systematic Gene Overexpression
Sopko, R., Huang, D., Preston, N., Chua, G., Papp, B., Kafadar, K. A., Snyder, M., Oliver, S. G., Cyert, M., Hughes, T. R., et al. (2006) · Molecular Cell · doi:10.1016/j.molcel.2005.12.011 · Cited by 650 · see citing works
Self-citationMinimum information requested in the annotation of biochemical models (MIRIAM)
Novère, N. L., Finney, A., Hucka, M., Bhalla, U. S., Campagne, F., Collado-Vides, J., Crampin, E. J., Halstead, M., Klipp, E., Mendes, P., et al. (2005) · Nature Biotechnology · doi:10.1038/nbt1156 · Cited by 622 · see citing works
Self-citationProgress in Metabolic Engineering of Saccharomyces cerevisiae
Nevoigt, E. (2008) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00025-07 · Cited by 597 · see citing works
Li, C., Donizelli, M., Rodriguez, N., Dharuri, H., Endler, L., Chelliah, V., Li, L., He, E., Henry, A., Stefan, M. I., et al. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-92 · Cited by 597 · see citing works
Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1492 · Cited by 588 · see citing works
Self-citationKACSER, H., Burns, J., KACSER, H., & Fell, D. A. (1995) · Biochemical Society Transactions · doi:10.1042/bst0230341 · Cited by 576 · see citing works
Kell, D. B., & Oliver, S. G. (2004) · BioEssays · doi:10.1002/bies.10385 · Cited by 535 · see citing works
Self-citationMetabolomics and systems biology: making sense of the soup
KELL, D. (2004) · Current Opinion in Microbiology · doi:10.1016/j.mib.2004.04.012 · Cited by 529 · see citing works
Self-citationExtent of Gene Duplication in the Genomes of Drosophila, Nematode, and Yeast
Gu, Z., Cavalcanti, A. R. O., Chen, F., Bouman, P., & Li, W. (2002) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a004079 · Cited by 496 · see citing works
Biochemical and genetic analysis of the yeast proteome with a movable ORF collectionPreprint
Gelperin, D. M., White, M. A., Wilkinson, M. L., Kon, Y., Kung, L. A., Wise, K. J., Lopez-Hoyo, N., Jiang, L., Piccirillo, S., Yu, H., et al. (2005) · Genes & Development · doi:10.1101/gad.1362105 · Cited by 479 · see citing works
Beynon, R. J., Doherty, M. K., Pratt, J. M., & Gaskell, S. J. (2005) · Nature Methods · doi:10.1038/nmeth774 · Cited by 472 · see citing works
Self-citationThe Size of the Nucleus Increases as Yeast Cells Grow
Jorgensen, P., Edgington, N. P., Schneider, B. L., Rupeš, I., Tyers, M., & Futcher, B. (2007) · Molecular Biology of the Cell · doi:10.1091/mbc.e06-10-0973 · Cited by 415 · see citing works
Web-based kinetic modelling using JWS Online
Olivier, B. G., & Snoep, J. L. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth200 · Cited by 326 · see citing works
Text mining and its potential applications in systems biology
Ananiadou, S., Kell, D. B., & Tsujii, J. (2006) · Trends in biotechnology · doi:10.1016/j.tibtech.2006.10.002 · Cited by 324 · see citing works
Self-citationControlled vocabularies and semantics in systems biology
Courtot, M., Juty, N., Knüpfer, C., Waltemath, D., Zhukova, A., Dräger, A., Dumontier, M., Finney, A., Golebiewski, M., Hastings, J., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.77 · Cited by 316 · see citing works
Self-citationBrown, M., Dunn, W. B., Dobson, P. D., Patel, Y., Winder, C. L., Francis‐McIntyre, S., Begley, P., Carroll, K. M., Broadhurst, D., Tseng, A., et al. (2009) · The Analyst · doi:10.1039/b901179j · Cited by 270 · see citing works
Self-citationHarris, C., Todd, R. W., Bungard, S. J., Lovitt, R. W., Morris, J. G., & Kell, D. B. (1987) · Enzyme and Microbial Technology · doi:10.1016/0141-0229(87)90075-5 · Cited by 268 · see citing works
Self-citationThe danger of metabolic pathways with turbo design
Teusink, B., Walsh, M. C., DAM, K. V., & Westerhoff, H. V. (1998) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(98)01205-5 · Cited by 247 · see citing works
Self-citationDiogo, M. M., Queiroz, J. A., Monteiro, G. A., Martins, S. A. M., Ferreira, G. N. M., & Prazeres, D. M. F. (2000) · Biotechnology and Bioengineering · doi:10.1002/(sici)1097-0290(20000605)68:5 · Cited by 196 · see citing works
Begley, P., Francis‐McIntyre, S., Dunn, W. B., Broadhurst, D., Halsall, A., Tseng, A., Knowles, J. D., HUSERMET Consortium, Goodacre, R., & Kell, D. B. (2009) · Analytical Chemistry · doi:10.1021/ac9011599 · Cited by 189 · see citing works
Self-citationControl analysis of metabolic systems
Burns, J. A., Cornish-Bowden, A. J., Groen, A. K., Heinrich, R., KACSER, H., Porteous, J. W., Rapoport, S. M., Rapoport, T. A., Stucki, J. W., Tager, J. M., et al. (1985) · Trends in Biochemical Sciences · doi:10.1016/0968-0004(85)90008-8 · Cited by 185 · see citing works
Self-citationAlbert, M., Haanstra, J. R., Hannaert, V., Roy, J. V., Opperdoes, F. R., Bakker, B. M., & Michels, P. A. M. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m502403200 · Cited by 163 · see citing works
Impact of systems biology on metabolic engineering ofSaccharomyces cerevisiae
Nielsen, J., & Jewett, M. C. (2007) · FEMS Yeast Research · doi:10.1111/j.1567-1364.2007.00302.x · Cited by 159 · see citing works
Hauf, J., Zimmermann, F. K., & Müller, S. (2000) · Enzyme and Microbial Technology · doi:10.1016/s0141-0229(00)00160-5 · Cited by 151 · see citing works
Reductive biotransformations of organic compounds by cells or enzymes of yeast
Ward, O. P., & Young, C. S. (1990) · Enzyme and Microbial Technology · doi:10.1016/0141-0229(90)90063-v · Cited by 151 · see citing works
Kell, D. B. (2006) · FEBS Journal · doi:10.1111/j.1742-4658.2006.05136.x · Cited by 147 · see citing works
Self-citationImproving metabolic flux predictions using absolute gene expression data
Lee, D. C. H., B, K. S. A., Dunn, W. B., Murabito, E., Winder, C. L., Kell, D. B., Mendes, P., & Swainston, N. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-73 · Cited by 145 · see citing works
Self-citationYeast genome duplication was followed by asynchronous differentiation of duplicated genes
Langkjær, R. B., Cliften, P. F., Johnston, M., & Piškur, J. (2003) · Nature · doi:10.1038/nature01419 · Cited by 142 · see citing works
Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network
Heavner, B., B, K. S. A., Barker, B., Mendes, P., & Walker, L. P. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-55 · Cited by 142 · see citing works
Self-citationMetabolic Control Analysis of Glycerol Synthesis inSaccharomyces cerevisiae
Cronwright, G. R., Rohwer, J. M., & Prior, B. A. (2002) · Applied and Environmental Microbiology · doi:10.1128/aem.68.9.4448-4456.2002 · Cited by 128 · see citing works
Quantifying heterogeneity: flow cytometry of bacterial cultures
Kell, D. B., Ryder, H. M., Kaprelyants, A. S., & Westerhoff, H. V. (1991) · Antonie van Leeuwenhoek · doi:10.1007/bf00430362 · Cited by 124 · see citing works
Self-citationThe renaissance of continuous culture in the post-genomics age
Bull, A. T. (2010) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-010-0816-4 · Cited by 119 · see citing works
Fifteen years of large scale metabolic modeling of yeast: Developments and impacts
Österlund, T., Nookaew, I., & Nielsen, J. (2011) · Biotechnology Advances · doi:10.1016/j.biotechadv.2011.07.021 · Cited by 117 · see citing works
Further developments towards a genome-scale metabolic model of yeast
Dobson, P. D., B, K. S. A., Jameson, D., Simeonidis, E., Lanthaler, K., Pir, P., Lü, C., Swainston, N., Dunn, W. B., Fisher, P., et al. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-145 · Cited by 110 · see citing works
Self-citationLebioda, L., & Stec, B. (1991) · Biochemistry · doi:10.1021/bi00225a012 · Cited by 109 · see citing works
van Eunen, K., Kiewiet, J., Westerhoff, H. V., & Bakker, B. M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002483 · Cited by 106 · see citing works
Self-citationWeusthuis, R. A., Visser, W. F., Pronk, J. T., Scheffers, W. A., & van Dijken, J. P. (1994) · Microbiology · doi:10.1099/00221287-140-4-703 · Cited by 106 · see citing works
Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis
Pritchard, L., & Kell, D. B. (2002) · European Journal of Biochemistry · doi:10.1046/j.1432-1033.2002.03055.x · Cited by 104 · see citing works
Self-citationSystems Biology: The elements and principles of Life
Westerhoff, H. V., Winder, C. L., Messiha, H. L., Simeonidis, E., Adamczyk, M., Verma, M., Bruggeman, F. J., & Dunn, W. B. (2009) · FEBS Letters · doi:10.1016/j.febslet.2009.11.018 · Cited by 102 · see citing works
Self-citationGarcía‐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
Self-citationThe relationship between enzyme activity, cell geometry, and fitness in Saccharomyces cerevisiae.
Weiss, R. L., Kukora, J. R., & Adams, J. (1975) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.72.3.794 · Cited by 78 · see citing works
de Jong, H., Ranquet, C., Ropers, D., Pinel, C., & Geiselmann, J. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-55 · Cited by 75 · see citing works
Davey, H. M., Davey, C. L., Woodward, A. M., Edmonds, A. N., Lee, A. W., & Kell, D. B. (1996) · Biosystems · doi:10.1016/0303-2647(95)01577-9 · Cited by 74 · see citing works
Self-citationAbsolute Quantification of the Glycolytic Pathway in Yeast:
Carroll, K. M., Simpson, D. M., Eyers, C. E., Cg, K., Brownridge, P., Dunn, W. B., Winder, C. L., Lanthaler, K., Pir, P., Malys, N., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.007633 · Cited by 73 · see citing works
Self-citationThe permittistat: a novel type of turbidostat
Markx, G. H., Davey, C. L., & Kell, D. B. (1991) · Journal of General Microbiology · doi:10.1099/00221287-137-4-735 · Cited by 72 · see citing works
Self-citationThe genetic control of growth rate: a systems biology study in yeast
Pir, P., Gutteridge, A., Wu, J., Rash, B. M., Kell, D. B., Zhang, N., & Oliver, S. G. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-4 · Cited by 58 · see citing works
Self-citationThe Role of Modeling in Systems Biology
Kell, D. B., & Knowles, J. D. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/6604.003.0003 · Cited by 54 · see citing works
Self-citationThe mechanism of cell differentiation in Bacillus subtilis
Iber, D., Clarkson, J., Yudkin, M. D., & Campbell, I. D. (2006) · Nature · doi:10.1038/nature04666 · Cited by 53 · 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
Comparative genomic assessment of novel broad‐spectrum targets for antibacterial drugs
White, T. A., & Kell, D. B. (2004) · Comparative and Functional Genomics · doi:10.1002/cfg.411 · Cited by 51 · see citing works
Self-citationBiswas, S. K. (2003) · Journal of Electron Microscopy · doi:10.1093/jmicro/52.2.133 · Cited by 46 · see citing works
How yeast re-programmes its transcriptional profile in response to different nutrient impulses
Dikicioǧlu, D., Karabekmez, M. E., Rash, B. M., Pir, P., Kırdar, B., & Oliver, S. G. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-148 · Cited by 46 · see citing works
Self-citationA probabilistic approach to identify putative drug targets in biochemical networks
Murabito, E., B, K. S. A., Swinton, J., Westerhoff, H. V., & Steuer, R. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0540 · Cited by 45 · see citing works
Self-citationMeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics
Spasić, ., Dunn, W. B., Velarde, G., Tseng, A., JENKINS, H. A., Hardy, N. W., Oliver, S. G., & Kell, D. B. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-281 · Cited by 44 · see citing works
Self-citationMalys, N. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh687 · Cited by 41 · see citing works
Self-citationEpistemic and methodological iteration in scientific research
Elliott, K. C. (2012) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2011.12.034 · Cited by 36 · see citing works
Systematic integration of experimental data and models in systems biology
Li, P., Dada, J. O., Jameson, D., Spasić, ., Swainston, N., Carroll, K. M., Dunn, W. B., Khan, F., Malys, N., Messiha, H. L., et al. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-582 · Cited by 36 · see citing works
Self-citationChapter 22 Enzyme Kinetics and Computational Modeling for Systems Biology
Mendes, P., Messiha, H. L., Malys, N., & Hoops, S. (2009) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(09)67022-1 · Cited by 31 · see citing works
Self-citationlibAnnotationSBML: a library for exploiting SBML annotations
Swainston, N., & Mendes, P. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp392 · Cited by 29 · see citing works
Self-citationThe Role of Modeling in Systems Biology
Kell, D. B., & Knowles, J. D. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262195485.003.0001 · Cited by 29 · see citing works
Self-citationMetabolic Channeling in Organized Enzyme Systems: Experiments and Models
Mendes, P., Kell, D. B., & Welch, G. R. (1995) · Advances in molecular and cell biology · doi:10.1016/s1569-2558(08)60246-5 · Cited by 28 · see citing works
Self-citationAdams, J. (1977) · Experimental Cell Research · doi:10.1016/0014-4827(77)90172-0 · Cited by 28 · see citing works
Enzyme kinetics informatics: from instrument to browser
Swainston, N., Golebiewski, M., Messiha, H., Malys, N., Kania, R., Kengne, S., Krebs, O., Mir, S., Sauer-Danzwith, H., Smallbone, K., et al. (2010) · The FEBS Journal · doi:10.1111/j.1742-4658.2010.07778.x · Cited by 24 · see citing works
Self-citationSample Preparation Related to the Intracellular Metabolome of Yeast
Dunn, W. B., & Winder, C. L. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00015-3 · Cited by 19 · see citing works
Self-citationBuilding a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae
B, K. S. A., Malys, N., Messiha, H. L., Wishart, J. A., & Simeonidis, E. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00018-9 · Cited by 18 · see citing works
Self-citationThe Use of Continuous Culture in Systems Biology Investigations
Winder, C. L., & Lanthaler, K. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00014-1 · Cited by 16 · see citing works
Self-citationMartins, A. M., Sha, W., Evans, C., Martino‐Catt, S. J., Mendes, P., & Shulaev, V. (2007) · Yeast · doi:10.1002/yea.1442 · Cited by 16 · see citing works
Self-citationProtein Production in Saccharomyces cerevisiae for Systems Biology Studies
Malys, N., Wishart, J. A., Oliver, S. G., & McCarthy, J. E. G. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00011-6 · Cited by 11 · see citing works
Self-citationSwainston, N., Jameson, D., & Carroll, K. M. (2010) · PROTEOMICS · doi:10.1002/pmic.201000454 · Cited by 11 · see citing works
Self-citationTheoretical and Experimental Analyses of Coupled Enzyme Reactions
Takagahara, I., YAMAUTI, J.-I., Fujii, K., Yamashita, J., & Horio, T. (1983) · The Journal of Biochemistry · doi:10.1093/oxfordjournals.jbchem.a134240 · Cited by 11 · see citing works
Integrative Information Management for Systems Biology
Swainston, N., Jameson, D., Li, P., Spasić, ., Mendes, P., & Paton, N. W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15120-0_13 · Cited by 7 · see citing works
Self-citationTowards a Full Quantitative Description of Yeast Metabolism
Messiha, H. L., Malys, N., & Carroll, K. M. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00012-8 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [25]van Hoek P. de Hulster E. van Dijken J.P. Pronk J.T. Fermentative capacity in high-cell-density fed-batch cultures of baker’s yeast Biotechnol. Bioeng. 68 2000 517 523 10797237
- [74]Lebioda L. Stec B. Mechanism of enolase: the crystal structure of enolase-Mg2+-2-phosphoglycerate/phosphoenolpyrvate complex resolution Biochemistry 30 1991 2817 2822 2007120
Cited by
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Davidi, D., Noor, E., Liebermeister, W., Bar-Even, A., Flamholz, A., Tummler, K., Barenholz, U., Goldenfeld, M., Shlomi, T., & Milo, R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1514240113 · Cited by 266 · see citing works
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Kell, D. B., Swainston, N., Pir, P., & Oliver, S. G. (2015) · Trends in biotechnology · doi:10.1016/j.tibtech.2015.02.001 · Cited by 195 · see citing works
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Khodayari, A., Zomorrodi, A. R., Liao, J. C., & Maranas, C. D. (2014) · Metabolic Engineering · doi:10.1016/j.ymben.2014.05.014 · Cited by 177 · see citing works
Path2Models: large-scale generation of computational models from biochemical pathway maps
Büchel, F., Rodríguez, N., Swainston, N., Wrzodek, C., Czauderna, T., Keller, R., Mittag, F., Schubert, M., Glont, M., Golebiewski, M., et al. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-116 · Cited by 173 · see citing works
Self-citationSaa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166 · see citing works
The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization
Alam, M. T., Olín‐Sandoval, V., Stincone, A., Keller, M. A., Zelezniak, A., Luisi, B. F., & Ralser, M. (2017) · Nature Communications · doi:10.1038/ncomms16018 · Cited by 136 · see citing works
Application of targeted mass spectrometry in bottom-up proteomics for systems biology research
Manes, N. P., & Nita‐Lazar, A. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.02.008 · Cited by 124 · see citing works
In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories
Maia, P., Rocha, M., & Rocha, I. (2015) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00014-15 · Cited by 113 · see citing works
Millard, P., B, K. S. A., & Mendes, P. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005396 · Cited by 108 · see citing works
Self-citationGrüning, N., Du, D., Keller, M. A., Luisi, B. F., & Ralser, M. (2014) · Open Biology · doi:10.1098/rsob.130232 · Cited by 107 · see citing works
Kim, O. D., Rocha, M., & Maia, P. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.01690 · Cited by 103 · see citing works
Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts
Zelezniak, A., Vowinckel, J., Capuano, F., Messner, C. B., Demichev, V., Polowsky, N., Mülleder, M., Kamrad, S., Klaus, B., Keller, M. A., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.08.001 · Cited by 100 · see citing works
Multiplicity of Steady States in Glycolysis and Shift of Metabolic State in Cultured Mammalian Cells
Mulukutla, B. C., Yongky, A., Grimm, S., Daoutidis, P., & Hu, W.-S. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121561 · Cited by 81 · see citing works
Eguchi, Y., Makanae, K., Hasunuma, T., Ishibashi, Y., Kito, K., & Moriya, H. (2018) · eLife · doi:10.7554/elife.34595 · Cited by 81 · see citing works
Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach
Saa, P. A., & Nielsen, L. K. (2016) · Scientific Reports · doi:10.1038/srep29635 · Cited by 80 · 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
Building kinetic models for metabolic engineering
Foster, C., Wang, L., Dinh, H. V., Suthers, P. F., & Maranas, C. D. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.11.010 · Cited by 70 · see citing works
Systems-based approaches to study immunometabolism
Purohit, V., Wagner, A., Yosef, N., & Kuchroo, V. K. (2022) · Cellular and Molecular Immunology · doi:10.1038/s41423-021-00783-9 · Cited by 62 · see citing works
Applications of computational modeling in metabolic engineering of yeast
Kerkhoven, E. J., Lahtvee, P., & Nielsen, J. (2014) · FEMS Yeast Research · doi:10.1111/1567-1364.12199 · Cited by 60 · see citing works
Hellgren, J., Godina, A., Nielsen, J., & Siewers, V. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.09.003 · Cited by 55 · see citing works
Computational Strategies for a System-Level Understanding of Metabolism
Cazzaniga, P., Damiani, C., Besozzi, D., Colombo, R., Nobile, M. S., Gaglio, D., Pescini, D., Molinari, S., Mauri, G., Alberghina, L., et al. (2014) · Metabolites · doi:10.3390/metabo4041034 · Cited by 54 · see citing works
Tang, W., Deshmukh, A. T., Haringa, C., Wang, G., van Gulik, W. M., van Winden, W. A., Reuß, M., Heijnen, J. J., Xia, J., Chu, J., et al. (2017) · Biotechnology and Bioengineering · doi:10.1002/bit.26294 · Cited by 54 · see citing works
Kinetic modeling of cell metabolism for microbial production
Costa, R. S., Hartmann, A., & Vinga, S. (2015) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2015.12.023 · Cited by 53 · see citing works
Construction and validation of a detailed kinetic model of glycolysis in Plasmodium falciparum
Penkler, G. P., du Toit, F., Adams, W. W., Rautenbach, M., Palm, D. C., van Niekerk, D. D., & Snoep, J. L. (2015) · FEBS Journal · doi:10.1111/febs.13237 · Cited by 48 · see citing works
Solis-Escalante, D., Kuijpers, N. G. A., Barrajón, N., van den Broek, M. A., Pronk, J. T., Daran, J.-M. G., & Daran-Lapujade, P. A. S. (2015) · Eukaryotic Cell · doi:10.1128/ec.00064-15 · Cited by 46 · see citing works
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
Targeting glycolysis in the malaria parasite Plasmodium falciparum
van Niekerk, D. D., Penkler, G. P., du Toit, F., & Snoep, J. L. (2015) · FEBS Journal · doi:10.1111/febs.13615 · Cited by 46 · see citing works
Multiscale models quantifying yeast physiology: towards a whole-cell model
Lu, H., Kerkhoven, E. J., & Nielsen, J. (2021) · Trends in biotechnology · doi:10.1016/j.tibtech.2021.06.010 · Cited by 43 · see citing works
Interrogating metabolism as an electron flow system
Zerfaß, C., Asally, M., & Soyer, O. S. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.10.001 · Cited by 44 · see citing works
biochem4j: Integrated and extensible biochemical knowledge through graph databases
Swainston, N., Batista-Navarro, R., Carbonell, P., Dobson, P. D., Dunstan, M. S., Jervis, A. J., Vinaixa, M., Williams, A. R., Ananiadou, S., Faulon, J., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179130 · Cited by 44 · see citing works
Self-citationThe importance and challenges of in vivo-like enzyme kinetics
van Eunen, K., & Bakker, B. M. (2014) · Perspectives in Science · doi:10.1016/j.pisc.2014.02.011 · Cited by 43 · see citing works
New types of experimental data shape the use of enzyme kinetics for dynamic network modeling
Tummler, K., Lubitz, T., Schelker, M., & Klipp, E. (2013) · FEBS Journal · doi:10.1111/febs.12525 · Cited by 42 · see citing works
The role of glycolysis-derived hexose phosphates in the induction of the Crabtree effect
Lemus, M. R., Roussarie, E., Hammad, N., Mougeolle, A., Ransac, S., Issa, R., Mazat, J., Uribe‐Carvajal, S., Rigoulet, M., & Devin, A. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.003672 · Cited by 42 · see citing works
Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes
Mišković, L., Tokić, M. S., Fengos, G., & Vassily Hatzimanikatis, A. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2015.08.019 · Cited by 41 · see citing works
Remli, M. A., Deris, S., Mohamad, M. S., Omatu, S., & Corchado, J. M. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.04.004 · Cited by 38 · see citing works
Li, J. J., Chew, G.-L., & Biggin, M. D. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx898 · Cited by 36 · see citing works
Engineering the glycolytic pathway: A potential approach for improvement of biocatalyst performance
Jojima, T., & Inui, M. (2015) · Bioengineered · doi:10.1080/21655979.2015.1111493 · Cited by 35 · see citing works
Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses
Wang, G., Haringa, C., Tang, W., Noorman, H. J., Chu, J., Zhuang, Y., & Zhang, S. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.27243 · Cited by 34 · see citing works
van Heerden, J. H., Bruggeman, F. J., & Teusink, B. (2014) · BioEssays · doi:10.1002/bies.201400108 · Cited by 34 · see citing works
Foster, C., Gopalakrishnan, S., Antoniewicz, M. R., & Maranas, C. D. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007319 · Cited by 29 · see citing works
Lao‐Martil, D., Schmitz, J. P. J., Teusink, B., & van Riel, N. A. W. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.03.005 · Cited by 27 · see citing works
Kumar, K., Venkatraman, V., & Bruheim, P. (2021) · Microbial Cell Factories · doi:10.1186/s12934-021-01557-8 · Cited by 27 · see citing works
Kinetic modeling of Chinese hamster ovary cell culture: factors and principles
Tang, P., Xu, J., Louey, A., Tan, Z., Yongky, A., Liang, S., Li, Z. J., Weng, Y., & Liu, S. (2020) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2019.1711015 · Cited by 26 · see citing works
Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale
Hu, M., Dinh, H. V., Shen, Y., Suthers, P. F., Foster, C., Call, C. M., Ye, X., Pratas, J., Fatma, Z., Zhao, H., et al. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.01.001 · Cited by 25 · see citing works
libChEBI: an API for accessing the ChEBI database
Swainston, N., Hastings, J., Dekker, A., Muthukrishnan, V., May, J. W., Steinbeck, C., & Mendes, P. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0123-9 · Cited by 25 · see citing works
Self-citationJin, Q., Wu, Q., Shapiro, B. M., & McKernan, S. E. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.02259-21 · Cited by 24 · see citing works
Matsuoka, Y., & Shimizu, K. (2015) · Bioresources and Bioprocessing · doi:10.1186/s40643-014-0031-7 · Cited by 23 · see citing works
Grigaitis, P., & Teusink, B. (2022) · FEBS Letters · doi:10.1002/1873-3468.14484 · Cited by 20 · see citing works
Traustason, B., Cheeks, M., & Dikicioǧlu, D. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20215464 · Cited by 20 · see citing works
Lao‐Martil, D., Verhagen, K. J. A., Schmitz, J. P. J., Teusink, B., Wahl, A., & van Riel, N. A. W. (2022) · Metabolites · doi:10.3390/metabo12010074 · Cited by 20 · see citing works
Applications of targeted proteomics in metabolic engineering: advances and opportunities
Yunus, I. S., & Lee, T. S. (2022) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102709 · Cited by 18 · see citing works
Sigmarsdóttir, Þ. B., McGarrity, S., Rolfsson, Ó., Yurkovich, J. T., & Sigurjónsson, Ó. E. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00239 · Cited by 17 · see citing works
Saavedra, E., González‐Chávez, Z., Moreno‐Sánchez, R., & Michels, P. A. M. (2018) · Current Medicinal Chemistry · doi:10.2174/0929867325666180917104242 · Cited by 17 · see citing works
Advances in biorenewables-resource-waste systems and modelling
Guo, M., Wu, C., Chapman, S. P., Yu, X., Vinestock, T., Hastings, A., Smith, P., & Shah, N. (2023) · Carbon Capture Science & Technology · doi:10.1016/j.ccst.2023.100142 · Cited by 16 · see citing works
Foster, C., Boorla, V. S., Dash, S., Gopalakrishnan, S., Jacobson, T. B., Olson, D. G., Amador‐Noguez, D., Lynd, L. R., & Maranas, C. D. (2022) · Metabolic Engineering · doi:10.1016/j.ymben.2021.12.012 · Cited by 16 · see citing works
Trauchessec, M., Jaquinod, M., Bonvalot, A., Brun, V., Bruley, C., Ropers, D., de Jong, H., Garin, J., Bestel‐Corre, G., & Ferro, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.032672 · Cited by 16 · see citing works
Węglarz‐Tomczak, E., Mondeel, T. D. G. A., Piebes, D. G. E., & Westerhoff, H. V. (2021) · Biomolecules · doi:10.3390/biom11040490 · Cited by 16 · see citing works
Self-citationpH dependencies of glycolytic enzymes of yeast under in vivo ‐like assay conditions
Luzia, L., Lao‐Martil, D., Savakis, P., van Heerden, J. H., van Riel, N. A. W., & Teusink, B. (2022) · FEBS Journal · doi:10.1111/febs.16459 · Cited by 14 · see citing works
Hu, M., Suthers, P. F., & Maranas, C. D. (2024) · Metabolic Engineering · doi:10.1016/j.ymben.2024.02.002 · Cited by 14 · see citing works
GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes
He, X. D., & Yan, M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05746-1 · Cited by 14 · see citing works
Uematsu, S., Ohno, S., Tanaka, K. Y., Hatano, A., Kokaji, T., Ito, Y., Kubota, H., Hironaka, K., Suzuki, Y., Matsumoto, M., et al. (2022) · iScience · doi:10.1016/j.isci.2022.103787 · Cited by 13 · see citing works
Umekawa, M., Nishikawa, A., Isono, N., & Karita, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-21400-3 · Cited by 14 · see citing works
Kito, K., Okada, M., Ishibashi, Y., Okada, S., & Ito, T. (2016) · PROTEOMICS · doi:10.1002/pmic.201500414 · Cited by 12 · see citing works
Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeastPreprint
Messiha, H. L., Kent, E., Malys, N., Carroll, K. M., Swainston, N., Mendes, P., & B, K. S. A. (2014) · doi:10.7287/peerj.preprints.146v4 · Cited by 14 · see citing works
Self-citationEnzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost
Alazmi, M. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1446063 · Cited by 13 · see citing works
Glycolysis model shows that allostery maintains high ATP and limits accumulation of intermediates
Choe, M., Einav, T., Phillips, R., & Titov, D. V. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.03.037 · Cited by 13 · see citing works
Systems biology of the modified branched Entner-Doudoroff pathway in Sulfolobus solfataricus
Figueiredo, A. S., Kouril, T., Esser, D., Haferkamp, P., Wieloch, P., Schomburg, D., Ruoff, P., Siebers, B., & Schaber, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180331 · Cited by 13 · see citing works
Identifying frequent patterns in biochemical reaction networks: a workflow
Lambusch, F., Waltemath, D., Wolkenhauer, O., Sandkuhl, K., Rosenke, C., & Henkel, R. (2018) · PeerJ Preprints · doi:10.1093/database/bay051 · Cited by 10 · see citing works
Visualising 2-simplex formation in metabolic reactions
Piangerelli, M., Maestri, S., & Merelli, E. (2020) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2020.107576 · Cited by 13 · see citing works
Model Balancing: A Search for In-Vivo Kinetic Constants and Consistent Metabolic States
Liebermeister, W., & Noor, Ε. (2021) · Metabolites · doi:10.3390/metabo11110749 · Cited by 11 · see citing works
Intelligent host engineering for metabolic flux optimisation in biotechnology
Munro, L. J., & Kell, D. B. (2021) · Biochemical Journal · doi:10.1042/bcj20210535 · Cited by 12 · see citing works
Self-citationThe Emi2 Protein of Saccharomyces cerevisiae is a Hexokinase Expressed under Glucose Limitation
Umekawa, M., Hamada, K., Isono, N., & Karita, S. (2020) · Journal of Applied Glycoscience · doi:10.5458/jag.jag.jag-2020_0007 · Cited by 12 · see citing works
Combined In-Cell NMR and Simulation Approach to Probe Redox-Dependent Pathway Control
Jensen, P. R., Matos, M. R. A., Sonnenschein, N., & Meier, S. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b00660 · Cited by 11 · see citing works
Kesten, D., Kummer, U., Sahle, S., & Hübner, K. (2015) · Biophysical Chemistry · doi:10.1016/j.bpc.2015.06.010 · Cited by 11 · see citing works
Zhang, Y., Kouril, T., Snoep, J. L., Siebers, B., Barberis, M., & Westerhoff, H. V. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18040876 · Cited by 11 · see citing works
Self-citationAgent-based models for detecting the driving forces of biomolecular interactions
Maestri, S., Merelli, E., & Pettini, M. (2022) · Scientific Reports · doi:10.1038/s41598-021-04205-8 · Cited by 10 · see citing works
Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learningPreprint
Kroll, A., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.10.516024 · Cited by 9 · see citing works
Jilani, S. B., Alahuhta, M., Bomble, Y. J., & Olson, D. G. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00405 · Cited by 7 · see citing works
Modeling and Model Simplification to Facilitate Biological Insights and Predictions
Eriksson, O., & Tegner, J. N. (2015) · Studies in mechanobiology, tissue engineering and biomaterials · doi:10.1007/978-3-319-21296-8_12 · Cited by 7 · see citing works
Analyzing and Designing Cell Factories with OptFlux
Vilaça, P., Maia, P., Giesteira, H. M. M., Rocha, I., & Rocha, M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7528-0_2 · Cited by 7 · see citing works
Hwang, J., Hari, A., Cheng, R., Gardner, J. G., & Lobo, D. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27544 · Cited by 7 · see citing works
Damiani, C., Colombo, R., Filippo, M. D., Pescini, D., & Mauri, G. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-57711-1_12 · Cited by 7 · see citing works
Bergdahl, B. M., Sonnenschein, N., Machado, D., Herrgård, M. J., & Förster, J. (2015) · Wiley eBooks · doi:10.1002/9783527697441.ch06 · Cited by 7 · see citing works
Gyesi, J. N., TomHon, P. M., Abdurraheem, A., Samoilenko, A., Scofield, S., Oladun, C., McBride, S. J., Curran, E., Pike, M., Kadari, K., et al. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c05076 · Cited by 6 · see citing works
Using Structural Kinetic Modeling To Identify Key Determinants of Stability in Reaction Networks
Carbonaro, N. J., & Thorpe, I. F. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b01852 · Cited by 6 · see citing works
A compiler for biological networks on silicon chips
Medley, K., Teo, J. J. Y., Woo, S. S., Hellerstein, J., Sarpeshkar, R., & Sauro, H. M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008063 · Cited by 6 · see citing works
Removal of Steryl Glucosides in Biodiesel Using Diatomaceous Earth as Adsorbent
Yuangsawad, R., Khambung, S., Laungthaleongpong, P., & Na‐Ranong, D. (2017) · doi:10.18690/978-961-286-048-6.12 · Cited by 4 · see citing works
Is distance from equilibrium a good indicator for a reaction’s flux control?
van Niekerk, D. D., Rust, E., Bruggeman, F. J., Westerhoff, H. V., & Snoep, J. L. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.104988 · Cited by 5 · see citing works
Self-citationKinetically accessible yield (KAY) for redirection of metabolism to produce exo-metabolites
Rivera, J. G. L., Theisen, M. K., Chen, P.-W., & Liao, J. C. (2017) · Metabolic Engineering · doi:10.1016/j.ymben.2017.03.011 · Cited by 4 · see citing works
Data integration strategies for whole-cell modeling
Tummler, K., & Klipp, E. (2024) · FEMS Yeast Research · doi:10.1093/femsyr/foae011 · Cited by 4 · see citing works
No Evidence That Protein Noise-Induced Epigenetic Epistasis Constrains Gene Expression Evolution
Boross, G. Z., & Papp, B. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw236 · Cited by 4 · see citing works
Colombo, R., Damiani, C., Mauri, G., & Pescini, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67834-4_9 · Cited by 4 · see citing works
Tangherloni, A., Nobile, M. S., Cazzaniga, P., Capitoli, G., Spolaor, S., Rundo, L., Mauri, G., & Besozzi, D. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009410 · Cited by 4 · see citing works
Modeling and Simulation of Batch Sugarcane Alcoholic Fermentation Using the Metabolic Model
Acorsi, R. L., Giovanni, M. Y. G. D., Olivo, J. E., & Andrade, C. M. G. (2022) · Fermentation · doi:10.3390/fermentation8020082 · Cited by 4 · see citing works
Lu, H., Chen, Y., Nielsen, J., & Kerkhoven, E. J. (2021) · Metabolic Engineering · doi:10.1002/9783527823468.ch5 · Cited by 4 · see citing works
Biocompatible SABRE Hyperpolarization of [1‐ 13 C]Ketoleucine for Cellular Metabolic Flux Sensing
TomHon, P. M., Gyesi, J. N., Abdurraheem, A., McBride, S. J., Samoilenko, A., Oladun, C., Curran, E., Pike, M., Welch, S. D., Scofield, S., et al. (2025) · Chemistry - A European Journal · doi:10.1002/chem.202502734 · Cited by 3 · see citing works
- Authors
- 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., Pir, P., Simeonidis, E., Spasić, I., Wishart, J., Weichart, D., Hayes, N. W., Jameson, D., Broomhead, D. S., Oliver, S. G., Gaskell, S. J., McCarthy, J. E. G., Paton, N. W., Westerhoff, H. V., Kell, D. B., & Mendes, P.
- Affiliations
- University of Manchester (GB)
- Manchester Academic Health Science Centre (GB)
- Babraham Institute (GB)
- University of Luxembourg (LU)
- University of Exeter (GB)
- University of Cambridge (GB)
- Queen Mary University of London (GB)
- Corresponding author
- Mendes, Pedro
- ORCID
- Venue
- FEBS Letters
- Publisher
- Wiley
- Published
- 2013-07-03
- Volume
- 587
- Issue
- 17
- Pages
- 2832-2841
- ISSN
- 0014-5793 (ISSN-L)
- 1873-3468
- PMC
- PMC3764422
- OpenAlex
- W2149741353
- Cited by
- 147
- References
- 88
- Citations by year
- 2026: 10 · 2025: 4 · 2024: 7 · 2023: 7 · 2022: 18 · 2021: 12 · 2020: 11 · 2019: 9 · 2018: 11 · 2017: 21 · 2016: 8 · 2015: 16 · 2014: 11 · 2013: 2
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- FEBS Letters (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- MIMS EPrints (University of Southampton) (acceptedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, other-oa)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by-nc-nd)
- KTUePubl (Repository of Kaunas University of Technology) (submittedVersion, other-oa)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- VU Research Portal (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- BBSRC (BB/J019259/1)
- EPSRC (BB/C008219/1)
- The Manchester Centre for Integrative Systems Biology (MCISB)
- EU FP7 (KBBE) (289434)
- BioPreDyn
- NIH (R01 GM080219)
- Manchester Centre for Integrative Systems Biology
- 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 › Fungal and yeast genetics research
- Keywords
- yeast glycolysis, Saccharomyces cerevisiae, enzyme kinetic parameters, isozymes, flux control, bottom-up modeling
- MeSH terms
- Computer Simulation, Glycolysis, Isoenzymes, Kinetics, Models, Biological, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Systems Biology, Metabolic Networks and Pathways
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 ›
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