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“Subsequently, alternative approaches were developed that require fewer parameters, including Boolean network modeling (Davidson et al., 2002) and constraint-based modeling (Orth et al., 2010; Thiele et al., 2009).”
From A Whole-Cell Computational Model Predicts Phenotype from Genotype (2012) · cites this paper as Orth et al., 2010
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“For instance, Flux Balance Analysis (FBA) models have been used to predict and understand the behavior of microbes given their environment (Orth et al., 2010;Thiele et al., 2013;Zelezniak et al., 2015).”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Orth et al., 2010
In the citing paper
“ME models have only been developed for Thermotoga maritima (Lerman et al, 2012) and E. coli (Orth et al, 2010) because they require detailed knowledge of all the steps of protein synthesis (protein maturation, protein folding, metal binding, etc.), which are not readily available for all organisms, and in particular for eukaryal cells.”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Orth et al, 2010
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“GECKO uses the flux balance analysis (FBA) approach (Orth et al, 2010).”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Orth et al, 2010
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“This approach, called flux balance analysis (Orth et al., 2010), enables comparison of metabolic fluxes with transcriptome and proteomedataandprovidesinformationaboutthe control of fluxes at the genome scale (Bordel et al., 2010).”
From Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast (2017) · cites this paper as Orth et al., 2010
In the citing paper
“Heckmann et al. 14 developed a k cat prediction model for enzymes in E. coli . The model relies on detailed, expert-crafted input features such as enzyme active site properties, metabolite concentrations, experimental conditions, and reaction fluxes calculated through flux balance analysis (FBA) 15 . It achieved a coefficient of determination R 2 ≈ 0.34 on an independent test set.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [15]
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Cites
Escherichia coli and Salmonella :cellular and molecular biology
Neidhardt, F. C. (1996) · ASM Press eBooks · Cited by 8,197
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
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
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835
Self-citationA protocol for generating a high-quality genome-scale metabolic reconstruction
Thiele, I., & Palsson, B. Ø. (2010) · Nature Protocols · doi:10.1038/nprot.2009.203 · Cited by 1,789
Self-citationFeist, 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 1,408
Self-citationAnalysis 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
Global reconstruction of the human metabolic network based on genomic and bibliomic data
Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610772104 · Cited by 1,374
Self-citationBurgard, A. P., Pharkya, P., & Maranas, C. D. (2003) · Biotechnology and Bioengineering · doi:10.1002/bit.10803 · Cited by 1,283
The effects of alternate optimal solutions in constraint-based genome-scale metabolic models
Mahadevan, R., & Schilling, C. H. (2003) · Metabolic Engineering · doi:10.1016/j.ymben.2003.09.002 · Cited by 1,203
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Artyomov, M. N., & den Bossche, J. V. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.09.013 · Cited by 217
Nogales, J., Mueller, J., Guðmundsson, S., Canalejo, F. J., Duque, E., Monk, J. M., Feist, A. M., Ramos, J. L., Niu, W., & Palsson, B. Ø. (2019) · Environmental Microbiology · doi:10.1111/1462-2920.14843 · Cited by 196
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Bekaert, M., Edger, P. P., Hudson, C. M., Pires, J. C., & Conant, G. C. (2012) · New Phytologist · doi:10.1111/j.1469-8137.2012.04302.x · Cited by 206
Bar‐Even, A., Noor, Ε., Flamholz, A. I., & Milo, R. (2012) · Biochimica et Biophysica Acta (BBA) - Bioenergetics · doi:10.1016/j.bbabio.2012.10.013 · Cited by 201
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Kumar, M., Babaei, P., Ji, B., & Nielsen, J. (2016) · Nutrition and Healthy Aging · doi:10.3233/nha-150002 · Cited by 211
Impact of gene expression noise on organismal fitness and the efficacy of natural selection
Wang, Z., & Zhang, J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1100059108 · Cited by 194
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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 192
Alternative Strategies for Microbial Remediation of Pollutants via Synthetic Biology
Jaiswal, S., & Shukla, P. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00808 · Cited by 195
Davis, R. E., Bioenergy Technologies Office (EE-3B) USDOE Office of Energy Efficiency and Renewable Energy (EERE), Grundl, N. J., Tao, L., Biddy, M., Tan, E. C. D., Beckham, G. T., Thompson, D. N., Thompson, D., & Roni, M. (2018) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/1483234 · Cited by 201
Lahtvee, P.-J., Sánchez, B. J., Smialowska, A., Kasvandik, S., Elsemman, I. E., Gatto, F., & Nielsen, J. (2017) · Cell Systems · doi:10.1016/j.cels.2017.03.003 · Cited by 193
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Rohn, H., Junker, A., Hartmann, A., Grafahrend‐Belau, E., Treutler, H., Klapperstück, M., Czauderna, T., Klukas, C., & Schreiber, F. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-139 · Cited by 192
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Wu, S., Liu, J., Liu, C., Yang, A., & Qiao, J. (2019) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-019-03326-8 · Cited by 188
Xu, X., Zarecki, R., Medina, S., Ofaim, S., Liu, X., Chen, C., Hu, S., Brom, D., Gat, D., Porob, S., et al. (2018) · The ISME Journal · doi:10.1038/s41396-018-0288-5 · Cited by 194
Ernst, M., Silva, D. B., Silva, R., Vêncio, R. Z. N., & Lopes, N. P. (2014) · Natural Product Reports · doi:10.1039/c3np70086k · Cited by 187
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Self-citationGaspar, P., Carvalho, A. L., Vinga, S., Santos, H., & Neves, A. R. (2013) · Biotechnology Advances · doi:10.1016/j.biotechadv.2013.03.011 · Cited by 185
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Wang, Y. X. R., & Huang, H. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.03.040 · Cited by 182
A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology
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Nagarajan, H., Sahin, M., Nogales, J., Latif, H., Lovley, D. R., Ebrahim, A., & Zengler, K. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-118 · Cited by 170
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Campodonico, M. A., Andrews, B. A., Asenjo, J. A., Palsson, B. Ø., & Feist, A. M. (2014) · Metabolic Engineering · doi:10.1016/j.ymben.2014.07.009 · Cited by 162
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Self-citationTorre, A. D. L., Metivier, A., Chu, F., Laurens, L. M. L., Beck, D. A. C., Pienkos, P. T., Lidstrom, M. E., & Kalyuzhnaya, M. (2015) · Microbial Cell Factories · doi:10.1186/s12934-015-0377-3 · Cited by 152
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Aite, M., Chevallier, M., Frioux, C., Trottier, C., Got, J., Cortés, M. P., Mendoza, S. N., Carrier, G., Dameron, O., Guillaudeux, N., et al. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006146 · Cited by 137
“Second, through formal mathematical formulation they can exploit this knowledge and predict the state of the network in different growth scenarios [2].”
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Perez De Souza, L., Alseekh, S., Brotman, Y., & Fernie, A. R. (2020) · Expert Review of Proteomics · doi:10.1080/14789450.2020.1766975 · Cited by 145
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Wishart, D. S., Li, C., Marcu, A., Badran, H., Pon, A., Budinski, Z., Patron, J., Lipton, D., Cao, X. D., Oler, E., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz861 · Cited by 148
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Seif, Y., Kavvas, E. S., Lachance, J., Yurkovich, J. T., Nuccio, S., Fang, X. D., Catoiu, E., Raffatellu, M., Palsson, B. Ø., & Monk, J. M. (2018) · Nature Communications · doi:10.1038/s41467-018-06112-5 · Cited by 136
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Nagarajan, H., Embree, M., Rotaru, A., Shrestha, P. M., Feist, A. M., Palsson, B. Ø., Lovley, D. R., & Zengler, K. (2013) · Nature Communications · doi:10.1038/ncomms3809 · Cited by 143
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Self-citationLeiby, N., & Marx, C. J. (2014) · PLoS Biology · doi:10.1371/journal.pbio.1001789 · Cited by 141
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Metadata
- Authors
- Orth, J. D., Thiele, I., & Palsson, B. Ø.
- Affiliations
- University of California San Diego (US)
- University of Iceland (IS)
- Corresponding author
- Palsson, Bernhard Ø
- Venue
- Nature Biotechnology
- Published
- 2010-03
- Volume
- 28
- Issue
- 3
- Pages
- 245-248
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3108565
- OpenAlex
- W2078355959
- Cited by
- 3,903
- References
- 44
- Citations by year
- 2026: 262 · 2025: 292 · 2024: 304 · 2023: 323 · 2022: 277 · 2021: 308 · 2020: 290 · 2019: 301 · 2018: 290 · 2017: 255 · 2016: 217 · 2015: 197 · 2014: 179 · 2013: 179 · 2012: 131
- FWCI
- 52.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM057089)
- 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
- flux balance analysis, metabolic networks, metabolic modeling, stoichiometric modeling, software toolbox
- MeSH terms
- Escherichia coli, Models, Biological, Oxygen, Phenotype, Biomass, Systems Biology, Metabolic Networks and Pathways
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