The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
What is flux balance analysis?
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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
In the citing paper
“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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Citations
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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Reķēna, A., Pinheiro, M. J., Bonturi, N., Belouah, I., Tammekivi, E., Herodes, K., Kerkhoven, E. J., & Lahtvee, P. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011009 · Cited by 33
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Formatting biological big data for modern machine learning in drug discovery
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Transparency in metabolic network reconstruction enables scalable biological discovery
Heavner, B. D., & Price, N. D. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.12.010 · Cited by 25
Klanchui, A., Dulsawat, S., Chaloemngam, K., Cheevadhanarak, S., Prommeenate, P., & Meechai, A. (2018) · Metabolites · doi:10.3390/metabo8040084 · Cited by 24
Population FBA predicts metabolic phenotypes in yeast
Labhsetwar, P., Melo, M. C. R., Cole, J. A., & Luthey-Schulten, Z. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005728 · Cited by 21
Application of Cyanobacteria as Chassis Cells in Synthetic Biology
Liu, X., Tang, K., & Hu, J. (2024) · Microorganisms · doi:10.3390/microorganisms12071375 · Cited by 20
A biochemical network modeling of a whole-cell
Burke, P. E. P., Campos, C. B. D. L., Costa, L. D. F., & Quiles, M. G. (2020) · Scientific Reports · doi:10.1038/s41598-020-70145-4 · Cited by 18
Resource allocation in mammalian systems
Baghdassarian, H. M., & Lewis, N. E. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108305 · Cited by 17
Abedpour, N., & Kollmann, M. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0232-5 · Cited by 7
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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