A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains
Open accessAbstract
Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).
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?
In genome-scale kinetic models of cellular metabolism, missing kcat values are typically addressed by sampling or fitting them to large datasets.
“In genome-scale kinetic models of cellular metabolism, this issue is typically addressed by either sampling missing k cat values or fitting them to large datasets 7 , 8 , 12 , 13 .”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [7]
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
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395 · see citing works
Encyclopedia of research design
(2011) · Choice Reviews Online · doi:10.5860/choice.48-2447 · Cited by 1,924 · 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
Analysis of optimality in natural and perturbed metabolic networks
Segrè, D., Vitkup, D., & Church, G. M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.232349399 · Cited by 1,375 · see citing works
Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Vassily Hatzimanikatis, A., & Palsson, B. Ø. (2007) · Molecular Systems Biology · doi:10.1038/msb4100155 · Cited by 1,367 · see citing works
Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003) · Biotechnology and Bioengineering · doi:10.1002/bit.10803 · Cited by 1,262 · see citing works
Self-citationThe 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
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
Dynamic modeling of the central carbon metabolism of Escherichia coli
Chassagnole, C., Noisommit‐Rizzi, N., Schmid, J. W., Mauch, K., & Reuß, M. (2002) · Biotechnology and Bioengineering · doi:10.1002/bit.10288 · Cited by 553 · see citing works
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
Brown, C. E. (1998) · Applied Multivariate Statistics in Geohydrology and Related Sciences · doi:10.1007/978-3-642-80328-4_13 · Cited by 528 · 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
Ranganathan, S., Suthers, P. F., & Maranas, C. D. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000744 · Cited by 420 · see citing works
Self-citationSchomburg, I., Chang, A., Placzek, S., Söhngen, C., Rother, M., Lang, M., Munaretto, C., Ulas, S., Stelzer, M., Grote, A., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1049 · Cited by 418 · see citing works
Flux Coupling Analysis of Genome-Scale Metabolic Network ReconstructionsPreprint
Burgard, A. P., Nikolaev, E. V., Schilling, C. H., & Maranas, C. D. (2004) · Genome Research · doi:10.1101/gr.1926504 · Cited by 383 · see citing works
Self-citationDe novo production of the flavonoid naringenin in engineered Saccharomyces cerevisiae
Koopman, F. W., Beekwilder, J., Crimi, B., van Houwelingen, A. M. M. L., Hall, R. D., Bosch, D., van Maris, A. J. A., Pronk, J. T., & Daran, J.-M. G. (2012) · Microbial Cell Factories · doi:10.1186/1475-2859-11-155 · Cited by 367 · see citing works
Global Gene Expression Profiling in Escherichia coli K12
Salmon, K. A., Hung, S., Steffen, N. R., Krupp, R., Baldi, P., Hatfield, G. W., & Gunsalus, R. P. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m414030200 · Cited by 365 · see citing works
Xu, P., Ranganathan, S., Fowler, Z. L., Maranas, C. D., & Koffas, M. A. G. (2011) · Metabolic Engineering · doi:10.1016/j.ymben.2011.06.008 · Cited by 346 · see citing works
Self-citationPathway 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
Bricks and blueprints: methods and standards for DNA assembly.
Casini, A., Storch, M., Baldwin, G. S., & Ellis, T. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm4014 · Cited by 286 · see citing works
SABIO-RK--database for biochemical reaction kinetics
Wittig, U., Kania, R., Golebiewski, M., Rey, M., Shi, L., Jong, L. M., Algaa, E., Weidemann, A., Sauer‐Danzwith, H., Mir, S., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1046 · Cited by 260 · see citing works
Global Gene Expression Profiling in Escherichia coli K12
Salmon, K. A., Hung, S., Mekjian, K., Baldi, P., Hatfield, G. W., & Gunsalus, R. P. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m213060200 · Cited by 255 · see citing works
Ensemble Modeling of Metabolic Networks
Tran, L. M., Rizk, M. L., & Liao, J. C. (2008) · Biophysical Journal · doi:10.1529/biophysj.108.135442 · Cited by 249 · see citing works
d-OptCom: Dynamic Multi-level and Multi-objective Metabolic Modeling of Microbial Communities
Zomorrodi, A. R., Islam, M. M., & Maranas, C. D. (2014) · ACS Synthetic Biology · doi:10.1021/sb4001307 · Cited by 208 · see citing works
Self-citationA kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data
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
Self-citationTowards a genome-scale kinetic model of cellular metabolism
B, K. S. A., Simeonidis, E., Swainston, N., & Mendes, P. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-6 · Cited by 171 · see citing works
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
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
Thermodynamic constraints shape the structure of carbon fixation pathways
Bar‐Even, A., Flamholz, A. I., Noor, Ε., & Milo, R. (2012) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2012.05.002 · Cited by 155 · see citing works
Zhao, J., & Shimizu, K. (2003) · Journal of Biotechnology · doi:10.1016/s0168-1656(02)00316-4 · Cited by 154 · see citing works
Chakrabarti, A., Mišković, L., Soh, K. C., & Vassily Hatzimanikatis, A. (2013) · Biotechnology Journal · doi:10.1002/biot.201300091 · Cited by 149 · see citing works
PRIDB: a protein-RNA interface database
Lewis, B. A., Walia, R. R., Terribilini, M., Ferguson, J., Zheng, C. Y., Honavar, V., & Dobbs, D. L. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1108 · Cited by 147 · see citing works
Genetics of pentose-phosphate pathway enzymes ofEscherichia coli K-12
Sprenger, G. A. (1995) · Archives of Microbiology · doi:10.1007/bf02529978 · Cited by 138 · see citing works
Cisek, K. K., Krochmal, M., Klein, J., & Mischak, H. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv364 · Cited by 128 · see citing works
Chen, Y., Hua, Q., Baba, T., Mori, H., & Shimizu, K. (2003) · Biotechnology and Bioengineering · doi:10.1002/bit.10692 · Cited by 127 · see citing works
Acyl-CoA synthetases: fatty acid uptake and metabolic channeling
Digel, M., Ehehalt, R., Stremmel, W., & Füllekrug, J. (2008) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-008-0003-3 · Cited by 107 · see citing works
Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics
Bordbar, A., McCloskey, D., Zielinski, D. C., Sonnenschein, N., Jamshidi, N., & Palsson, B. Ø. (2015) · Cell Systems · doi:10.1016/j.cels.2015.10.003 · Cited by 94 · see citing works
Zhao, J., Baba, T., Mori, H., & Shimizu, K. (2004) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-003-1458-5 · Cited by 93 · see citing works
13C metabolic flux analysis at a genome-scale
Gopalakrishnan, S., & Maranas, C. D. (2015) · Metabolic Engineering · doi:10.1016/j.ymben.2015.08.006 · Cited by 92 · see citing works
Self-citationMetabolic engineering of Saccharomyces cerevisiae for the production of triacetic acid lactone
Cárdenas, J., & Silva, N. A. D. (2014) · Metabolic Engineering · doi:10.1016/j.ymben.2014.07.008 · Cited by 92 · see citing works
Kotlarz, D., Garreau, H., & Buc, H. (1975) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/0304-4165(75)90232-9 · Cited by 88 · see citing works
Leighty, R. W., & Antoniewicz, M. R. (2012) · Metabolic Engineering · doi:10.1016/j.ymben.2012.06.003 · Cited by 85 · see citing works
Basse, M.-J., Betzi, S., Morelli, X., & Roche, P. (2016) · Database · doi:10.1093/database/baw007 · Cited by 83 · see citing works
Kadir, T. A. A., Mannan, A. A., Kierzek, A. M., McFadden, J., & Shimizu, K. (2010) · Microbial Cell Factories · doi:10.1186/1475-2859-9-88 · Cited by 76 · see citing works
Modeling of uncertainties in biochemical reactions
Mišković, L., & Vassily Hatzimanikatis, A. (2010) · Biotechnology and Bioengineering · doi:10.1002/bit.22932 · Cited by 74 · see citing works
Fendt, S., Oliveira, A. P., Christen, S., Picotti, P., Dechant, R. C., & Sauer, U. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.91 · Cited by 67 · see citing works
Somewhat in control — the role of transcription in regulating microbial metabolic fluxes
Kochanowski, K., Sauer, U., & Chubukov, V. (2013) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2013.03.014 · Cited by 59 · see citing works
Metabolic ensemble modeling for strain engineers
Tan, Y., & Liao, J. C. (2011) · Biotechnology Journal · doi:10.1002/biot.201100186 · Cited by 57 · see citing works
Khodayari, A., Chowdhury, A., & Maranas, C. D. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2014.00076 · Cited by 54 · see citing works
Self-citationThe MetaboLights repository: curation challenges in metabolomics
Salek, R. M., Haug, K., Conesa, P., Hastings, J., Williams, M., Mahendraker, T., Maguire, E., Gonzalez-Beltran, A. N., Rocca‐Serra, P., Sansone, S., et al. (2013) · Database · doi:10.1093/database/bat029 · Cited by 54 · see citing works
Kabir, M. M., Ho, P. Y., & Shimizu, K. (2005) · Biochemical Engineering Journal · doi:10.1016/j.bej.2005.05.010 · Cited by 52 · see citing works
Kabir, M. M., & Shimizu, K. (2004) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-004-1627-1 · Cited by 48 · see citing works
The LASER database: Formalizing design rules for metabolic engineering
Winkler, J. D., Halweg‐Edwards, A. L., & Gill, R. T. (2015) · Metabolic Engineering Communications · doi:10.1016/j.meteno.2015.06.003 · Cited by 42 · see citing works
Saa, P. A., & Nielsen, L. K. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004195 · Cited by 42 · see citing works
Zhang, Z., Shen, T., Rui, B., Zhou, W., Zhou, X., Shang, C., Xin, C., Liu, X., Li, G., Jiang, J., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1137 · Cited by 42 · see citing works
SHEARER, G. B., Lee, J. C., Koo, J., & Kohl, D. H. (2005) · FEBS Journal · doi:10.1111/j.1742-4658.2005.04712.x · Cited by 40 · see citing works
Improving prediction fidelity of cellular metabolism with kinetic descriptions
Chowdhury, A., Khodayari, A., & Maranas, C. D. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2015.08.011 · Cited by 37 · see citing works
Self-citationPeng, L., & Shimizu, K. (2005) · Enzyme and Microbial Technology · doi:10.1016/j.enzmictec.2005.07.019 · Cited by 35 · see citing works
The genome editing toolbox: a spectrum of approaches for targeted modification
Cheng, J. K., & Alper, H. S. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.06.005 · Cited by 34 · see citing works
Lin, F., Chen, Y., Levine, R. B., Lee, K., Yuan, Y., & Lin, X. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078595 · Cited by 29 · see citing works
Choudhary, M. K., Yoon, J. M., González, R., & Shanks, J. V. (2011) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-010-0449-5 · Cited by 19 · see citing works
Software‐aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery
Cece, E. N., Fontaine, F., Plasencia, G., Teppner, M., Brink, A., Pähler, A., & Zamora, I. (2015) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.7429 · Cited by 12 · see citing works
Strotksaya, A., Semenova, E., Savitskaya, E. E., & Severinov, K. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2687-9_9 · Cited by 9 · see citing works
Hoque, M. A., Siddiquee, K., & Shimizu, K. (2004) · Biochemical Engineering Journal · doi:10.1016/j.bej.2003.10.005 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Cautha S. C.. in 12th IFAC Symposium on Computer Applications in Biotechnology 221–226 (Mumbai, India, 2013).
- Cornish-Bowden A. in Fundamentals of Enzyme Kinetics 4th edn Wiley (2012).
Cited by
Sorted by citations
Scientific, sustainability and regulatory challenges of cultured meat
Post, M. J., Levenberg, S., Kaplan, D. L., Genovese, N. J., Fu, J., Bryant, C. J., Negowetti, N. E., Verzijden, K., & Moutsatsou, P. (2020) · Nature Food · doi:10.1038/s43016-020-0112-z · Cited by 807 · see citing works
Lian, J., Mishra, S., & Zhao, H. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.04.011 · Cited by 332 · see citing works
Seaver, S. M. D., Liu, F., Zhang, Q., Jeffryes, J. G., Faria, J. P., Edirisinghe, J. N., Mundy, M. B., Chia, N., Noor, Ε., Noor, Ε., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa746 · Cited by 267 · see citing works
Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
Rosario, S. R., Long, M. D., Affronti, H. C., Rowsam, A. M., Eng, K. H., & Smiraglia, D. J. (2018) · Nature Communications · doi:10.1038/s41467-018-07232-8 · Cited by 268 · see citing works
A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
Costello, Z., & Martín, H. G. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0054-3 · Cited by 238 · 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
Metabolic Engineering: Methodologies and Applications
Volk, M., Tran, V. G., Tan, S., Mishra, S., Fatma, Z., Boob, A. G., Li, H., Xue, P., Martin, T. A., & Zhao, H. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00403 · Cited by 204 · 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
Saa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166 · see citing works
A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications
Antoniewicz, M. R. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.11.002 · Cited by 156 · see citing works
Simultaneous cross-evaluation of heterogeneous <i>E. coli</i> datasets via mechanistic simulation
Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I., et al. (2020) · Science · doi:10.1126/science.aav3751 · Cited by 151 · see citing works
Fröhlich, F., Kessler, T., Weindl, D., Shadrin, A., Schmiester, L., Hache, H., Muradyan, A., Schütte, M., Lim, J.-H., Heinig, M., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.013 · Cited by 143 · see citing works
AI applications in functional genomics
Caudai, C., Galizia, A., Geraci, F., Pera, L. L., Morea, V., Salerno, E., Via, A., & Colombo, T. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.10.009 · Cited by 150 · see citing works
A review of computational tools for design and reconstruction of metabolic pathways
Wang, L., Dash, S., Ng, C. Y., & Maranas, C. D. (2017) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2017.11.002 · Cited by 134 · see citing works
Self-citationBiosystems Design by Machine Learning
Volk, M., Lourentzou, I., Mishra, S., Vo, L. T., Zhai, C., & Zhao, H. (2020) · ACS Synthetic Biology · doi:10.1021/acssynbio.0c00129 · Cited by 139 · 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
Modular Metabolic Engineering for Biobased Chemical Production
Lu, H., Villada, J. C., & Lee, P. K. H. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.07.003 · Cited by 127 · see citing works
Zhang, Y., Liu, D., & Chen, Z. (2017) · Biotechnology for Biofuels · doi:10.1186/s13068-017-0992-9 · Cited by 101 · see citing works
Leveraging knowledge engineering and machine learning for microbial bio-manufacturing
Oyetunde, T., Bao, F. S., Chen, J.-W., Martín, H. G., & Tang, Y. (2018) · Biotechnology Advances · doi:10.1016/j.biotechadv.2018.04.008 · Cited by 91 · see citing works
Angione, C. (2019) · BioMed Research International · doi:10.1155/2019/8304260 · Cited by 88 · see citing works
Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis
Long, C. P., Gonzalez, J. E., Cipolla, R. M., & Antoniewicz, M. R. (2017) · Metabolic Engineering · doi:10.1016/j.ymben.2017.10.008 · Cited by 83 · see citing works
Choudhury, S., Moret, M., Salvy, P., Weilandt, D. R., Hatzimanikatis, V., & Miskovic, L. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00519-y · Cited by 80 · 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
Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches
Yasemi, M., & Jolicœur, M. (2021) · Processes · doi:10.3390/pr9020322 · Cited by 84 · see citing works
Heckmann, D., Campeau, A., Lloyd, C. J., Phaneuf, P. V., Hefner, Y., Carrillo-Terrazas, M., Feist, A. M., Gonzalez, D. J., & Palsson, B. Ø. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001562117 · Cited by 78 · see citing works
Reznik, E., Christodoulou, D., Goldford, J. E., Briars, E. A., Sauer, U., Segrè, D., & Noor, Ε. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.08.066 · Cited by 80 · see citing works
Dinh, H. V., Suthers, P. F., Chan, S. H. J., Shen, Y., Xiao, T., Deewan, A., Jagtap, S. S., Zhao, H., Rao, C. V., Rabinowitz, J. D., et al. (2019) · Metabolic Engineering Communications · doi:10.1016/j.mec.2019.e00101 · Cited by 78 · see citing works
Self-citationAutomated engineering of synthetic metabolic pathways for efficient biomanufacturing
Otero‐Muras, I., & Carbonell, P. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.11.012 · Cited by 77 · see citing works
Metabolic engineering for sustainability and health
Kim, G. B., Choi, S. Y., Cho, I., Ahn, D., & Lee, S. Y. (2023) · Trends in biotechnology · doi:10.1016/j.tibtech.2022.12.014 · Cited by 75 · 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
Enzyme 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
Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis
Volkova, S., Matos, M. R. A., Mattanovich, M., & de Mas, I. M. (2020) · Metabolites · doi:10.3390/metabo10080303 · Cited by 75 · see citing works
Synthetic methylotrophy: Strategies to assimilate methanol for growth and chemicals production
Antoniewicz, M. R. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.07.001 · Cited by 74 · see citing works
Ye, C., Luo, Q., Guo, L., Gao, C., Xu, N., Zhang, L., Liu, L., & Chen, X. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27485 · Cited by 68 · see citing works
Ataman, M., Gardiol, D. F. H., Fengos, G., & Vassily Hatzimanikatis, A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005444 · Cited by 73 · see citing works
Comparative analysis of high butanol tolerance and production in clostridia
Patáková, P., Kolek, J., Sedlář, K., Koščová, P., Branská, B., Kupkova, K., Paulová, L., & Provazník, I. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.12.004 · Cited by 72 · see citing works
GotEnzymes: an extensive database of enzyme parameter predictions
Li, F., Chen, Y., Anton, M., & Nielsen, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac831 · Cited by 71 · 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
Self-citationChoudhury, S., Narayanan, B., Moret, M., Hatzimanikatis, V., & Miskovic, L. (2024) · Nature Catalysis · doi:10.1038/s41929-024-01220-6 · Cited by 67 · see citing works
New synthetic biology tools for metabolic control
Lv, X., Hueso‐Gil, Á., Bi, X., Wu, Y., Liu, Y., Liu, L., & Ledesma‐Amaro, R. (2022) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102724 · Cited by 67 · see citing works
Genome-scale metabolic network models: from first-generation to next-generation
Ye, C., Wei, X., Shi, T., Sun, X., Xu, N., Gao, C., & Zou, W. (2022) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-022-12066-y · Cited by 64 · see citing works
Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris
Zúñiga, C., Levering, J., Antoniewicz, M. R., Guarnieri, M. T., Betenbaugh, M. J., & Zengler, K. (2017) · PLANT PHYSIOLOGY · doi:10.1104/pp.17.00605 · Cited by 66 · see citing works
Systems and synthetic biology tools for advanced bioproduction hosts
Chen, Y., Banerjee, D., Mukhopadhyay, A., & Petzold, C. J. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.12.007 · Cited by 64 · see citing works
Gopalakrishnan, S., Pakrasi, H. B., & Maranas, C. D. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.03.008 · Cited by 63 · see citing works
Self-citationMetabolic kinetic modeling provides insight into complex biological questions, but hurdles remain
Strutz, J., Martin, J. P., Greene, J. L., Broadbelt, L. J., & Tyo, K. E. J. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.02.005 · Cited by 62 · see citing works
Emerging methods for genome-scale metabolic modeling of microbial communities
Tarzi, C., Zampieri, G., Sullivan, N., & Angione, C. (2024) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2024.02.018 · Cited by 62 · see citing works
Modular design: Implementing proven engineering principles in biotechnology
Garcia, S., & Trinh, C. T. (2019) · Biotechnology Advances · doi:10.1016/j.biotechadv.2019.06.002 · Cited by 59 · see citing works
van Lent, P., Schmitz, J. P. J., & Abeel, T. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00186 · Cited by 56 · see citing works
Cell factory design with advanced metabolic modelling empowered by artificial intelligence
Lu, H., Xiao, L., Liao, W., Yan, X., & Nielsen, J. (2024) · Metabolic Engineering · doi:10.1016/j.ymben.2024.07.003 · Cited by 57 · see citing works
Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models
Clark, T. J., Guo, L., Morgan, J. A., & Schwender, J. (2020) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-050718-100221 · Cited by 55 · see citing works
K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data
Gopalakrishnan, S., Dash, S., & Maranas, C. D. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2020.03.001 · Cited by 53 · see citing works
Self-citationLong, C. P., & Antoniewicz, M. R. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2019.08.003 · Cited by 51 · see citing works
Strain design optimization using reinforcement learning
Sabzevari, M., Szedmák, S., Penttilä, M. E., Jouhten, P., & Rousu, J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010177 · Cited by 47 · see citing works
Ang, K. S., Lakshmanan, M., Lee, N., & Lee, D. (2018) · Current Genomics · doi:10.2174/1389202919666180911144055 · Cited by 47 · see citing works
Amann, T., Schmieder, V., Faustrup Kildegaard, H., Borth, N., & Andersen, M. R. (2019) · Biotechnology and Bioengineering · doi:10.1002/bit.27101 · Cited by 46 · see citing works
AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications
Khan, M. F., & Khan, M. T. (2025) · Molecules · doi:10.3390/molecules31010045 · Cited by 49 · see citing works
Wang, Z., Danziger, S. A., Heavner, B., Ma, S., Smith, J. J., Song, L., Herricks, T., Simeonidis, E., Baliga, N. S., Aitchison, J. D., et al. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005489 · 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
Nishiguchi, H., Hiasa, N., Uebayashi, K., Liao, J. C., Shimizu, H., & Matsuda, F. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2019.01.004 · Cited by 43 · see citing works
Karlsen, S. T., Rau, M. H., Sánchez, B. J., Jensen, K. L., & Zeidan, A. A. (2023) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuad030 · Cited by 40 · see citing works
Modeling lignin biosynthesis: a pathway to renewable chemicals
Rao, X., & Barros, J. (2023) · Trends in Plant Science · doi:10.1016/j.tplants.2023.09.011 · Cited by 42 · see citing works
Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole‐cell model WM_S288C
Ye, C., Xu, N., Gao, C., Liu, G., Xu, J., Zhang, W., Chen, X., Nielsen, J., & Liu, L. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27298 · Cited by 40 · see citing works
MinGenome: An In Silico Top-Down Approach for the Synthesis of Minimized Genomes
Wang, L., & Maranas, C. D. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.7b00296 · Cited by 41 · see citing works
Self-citationSahin, A., Weilandt, D., & Vassily Hatzimanikatis, A. (2023) · Nature Communications · doi:10.1038/s41467-023-38159-4 · Cited by 39 · see citing works
Huang, Z., Lee, D., & Yoon, S. (2017) · Biotechnology and Bioengineering · doi:10.1002/bit.26384 · Cited by 39 · see citing works
Tuure, H., Fengos, G., Ataman, M., Mišković, L., & Vassily Hatzimanikatis, A. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.10.005 · Cited by 38 · see citing works
Suthers, P. F., Foster, C., Sarkar, D., Wang, L., & Maranas, C. D. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.11.013 · Cited by 38 · see citing works
Self-citationDigital models in biotechnology: Towards multi-scale integration and implementation
Hartmann, F. S. F., Udugama, I. A., Seibold, G. M., Sugiyama, H., & Gernaey, K. V. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.108015 · 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
Approaches to Computational Strain Design in the Multiomics Era
John, P. C. S., & Bomble, Y. J. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.00597 · Cited by 37 · 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
Active and machine learning-based approaches to rapidly enhance microbial chemical production
Kumar, P., Adamczyk, P., Zhang, X., Andrade, R. B., Romero, P. A., Ramanathan, P., & Reed, J. L. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2021.06.009 · Cited by 35 · see citing works
Kinetic ensemble model of gas fermenting Clostridium autoethanogenum for improved ethanol production
Greene, J. L., Daniell, J., Köpke, M., Broadbelt, L. J., & Tyo, K. E. J. (2019) · Biochemical Engineering Journal · doi:10.1016/j.bej.2019.04.021 · Cited by 34 · see citing works
Horvath, N. M., Vilkhovoy, M., Wayman, J. A., Calhoun, K. A., Swartz, J. R., & Varner, J. D. (2019) · Metabolic Engineering Communications · doi:10.1016/j.mec.2019.e00113 · Cited by 33 · see citing works
Kurata, H., & Sugimoto, Y. (2017) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2017.09.005 · Cited by 33 · see citing works
The E. coli Whole-Cell Modeling Project
Sun, G., Ahn-Horst, T. A., & Covert, M. W. (2021) · EcoSal Plus · doi:10.1128/ecosalplus.esp-0001-2020 · Cited by 33 · see citing works
Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges
Bi, X., Liu, Y., Li, J., Du, G., Lv, X., & Liu, L. (2022) · Biomolecules · doi:10.3390/biom12050721 · Cited by 31 · see citing works
A dynamic kinetic model captures cell-free metabolism for improved butanol production
Martin, J. P., Rasor, B. J., DeBonis, J., Karim, A. S., Jewett, M. C., Tyo, K. E. J., & Broadbelt, L. J. (2022) · Metabolic Engineering · doi:10.1016/j.ymben.2023.01.009 · Cited by 32 · see citing works
Towards next-generation model microorganism chassis for biomanufacturing
Liu, Y., Su, A., Li, J., Ledesma‐Amaro, R., Xu, P., Du, G., & Liu, L. (2020) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-020-10902-7 · Cited by 31 · see citing works
EITLEM-Kinetics: A deep-learning framework for kinetic parameter prediction of mutant enzymes
Shen, X., Cui, Z., Long, J., Zhang, S., Chen, B., & Tan, T. (2024) · Chem Catalysis · doi:10.1016/j.checat.2024.101094 · Cited by 34 · see citing works
Dash, S., Khodayari, A., Zhou, J., Holwerda, E. K., Olson, D. G., Lynd, L. R., & Maranas, C. D. (2017) · Biotechnology for Biofuels · doi:10.1186/s13068-017-0792-2 · Cited by 31 · see citing works
Self-citationCrosstalk 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
Simulating biological processes: stochastic physics from whole cells to colonies
Earnest, T. M., Cole, J. A., & Luthey-Schulten, Z. A. (2018) · Reports on Progress in Physics · doi:10.1088/1361-6633/aaae2c · Cited by 29 · 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
Self-citationTummler, K., & Klipp, E. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.11.003 · Cited by 28 · see citing works
Kinetic modeling of metabolism: Present and future
Islam, M. M., Schroeder, W. L., & Saha, R. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.04.003 · Cited by 28 · see citing works
Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities
Cao, X., Hamilton, J. J., & Venturelli, O. S. (2018) · Biochemistry · doi:10.1021/acs.biochem.8b01006 · Cited by 28 · see citing works
Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties
Mišković, L., Béal, J., Moret, M., & Vassily Hatzimanikatis, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007242 · Cited by 27 · 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
Self-citationEscherichia coli as a model organism for systems metabolic engineering
Adamczyk, P., & Reed, J. L. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.11.001 · Cited by 25 · see citing works
Kang, C. K., Shin, J., Cha, Y., Kim, M. S., Choi, M. S., Kim, T., Park, Y.-K., & Choi, Y. J. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128455 · Cited by 24 · 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
Du, H., Li, M., & Liu, Y. (2023) · Quantitative Biology · doi:10.15302/j-qb-022-0313 · Cited by 23 · see citing works
Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics
Moulin, C., Tournier, L., & Pérès, S. (2021) · Processes · doi:10.3390/pr9101701 · Cited by 23 · see citing works
Weilandt, D., Salvy, P., Masid, M., Fengos, G., Denhardt-Erikson, R., Hosseini, Z., & Vassily Hatzimanikatis, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac787 · Cited by 23 · see citing works
Gopalakrishnan, S., Joshi, C. J., Valderrama-Gómez, M. Á., Içten, E., Rolandi, P. A., Johnson, W., Kontoravdi, C., & Lewis, N. E. (2022) · Metabolic Engineering · doi:10.1016/j.ymben.2022.12.003 · Cited by 22 · see citing works
Ghadermazi, P., & Chan, S. H. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae003 · Cited by 23 · see citing works
The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions
Toumpe, I., Choudhury, S., Vassily Hatzimanikatis, A., & Mišković, L. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00868 · Cited by 21 · see citing works
Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana
Küken, A., Gennermann, K., & Nikoloski, Z. (2020) · The Plant Journal · doi:10.1111/tpj.14890 · Cited by 22 · see citing works
Acceleration Strategies to Enhance Metabolic Ensemble Modeling Performance
Greene, J. L., Wäechter, A., Tyo, K. E. J., & Broadbelt, L. J. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.07.018 · Cited by 21 · see citing works
- Authors
- Khodayari, A., & Maranas, C. D.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Maranas, Costas D.
- ORCID
- Maranas, Costas D.
- Venue
- Nature Communications
- Published
- 2016-12-20
- Volume
- 7
- Issue
- 1
- Pages
- 13806-13806
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5187423
- OpenAlex
- W2561960911
- Cited by
- 230
- References
- 64
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 18 · 2023: 23 · 2022: 21 · 2021: 20 · 2020: 32 · 2019: 29 · 2018: 39 · 2017: 24
- FWCI
- 15.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Energy (DESC0012377)
- University of Pennsylvania
- Pennsylvania State University (DE-SC0012377)
- Institute for Computational and Data Sciences, Pennsylvania State University
- 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 › Bioinformatics and Genomic Networks
- Keywords
- Escherichia coli, kinetic metabolic models, genome-scale metabolic modeling, fluxomics, mutant strain, product yield prediction, metabolic engineering
- MeSH terms
- Escherichia coli, Kinetics, Models, Biological, Mutation, Genome, Bacterial, Metabolome, Metabolic Flux Analysis
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
