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?
Open accessOverview
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
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 1,010
Tanaka, K., Henry, C. S., Zinner, J. F., Jolivet, E., Cohoon, M., Xia, F., Bidnenko, V., Ehrlich, S. D., Stevens, R. L., & Noirot, P. H. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks963 · Cited by 99
Construction and completion of flux balance models from pathway databases
Latendresse, M., Krummenacker, M., Trupp, M., & Karp, P. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr681 · Cited by 99
Computational Approaches for Integrative Analysis of the Metabolome and Microbiome
Chong, J., & Xia, J. (2017) · Metabolites · doi:10.3390/metabo7040062 · Cited by 104
Smiatek, J., Jung, A., & Bluhmki, E. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.05.008 · Cited by 98
Designing biological systems: Systems Engineering meets Synthetic Biology
Rollié, S., Mangold, M., & Sundmacher, K. (2011) · Chemical Engineering Science · doi:10.1016/j.ces.2011.10.068 · Cited by 100
Integration of metabolomics data into metabolic networks
Töpfer, N., Kleeßen, S., & Nikoloski, Z. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.00049 · Cited by 102
Bordbar, A., Johansson, P. I., Paglia, G., Harrison, S. J., Wichuk, K., Magnúsdóttir, M., Valgeirsdóttir, S., Gybel‐Brask, M., Ostrowski, S. R., Palsson, S., et al. (2016) · Transfusion · doi:10.1111/trf.13460 · Cited by 96
Self-citationHendry, J. I., Prasannan, C. B., Joshi, A. A., Dasgupta, S., & Wangikar, P. P. (2016) · Bioresource Technology · doi:10.1016/j.biortech.2016.02.128 · Cited by 94
Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models
Chan, S. H. J., Cai, J., Wang, L., Simons, M., & Maranas, C. D. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx453 · Cited by 98
Greenhalgh, K., Ramiro‐Garcia, J., Heinken, A., Ullmann, P., Bintener, T., Pacheco, M. P., Bagińska, J., Shah, P., Frachet, A., Halder, R., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.04.001 · Cited by 98
Self-citationdiCenzo, G. C., Tesi, M., Pfau, T., Mengoni, A., & Fondi, M. (2020) · Nature Communications · doi:10.1038/s41467-020-16484-2 · Cited by 96
Computational modeling of metabolism in microbial communities on a genome-scale
Colarusso, A. V., Goodchild-Michelman, I. M., Rayle, M., & Zomorrodi, A. R. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.04.001 · Cited by 98
Jia, D., Park, J. H., Kaur, H., Jung, K. H., Yang, S., Tripathi, S., Galbraith, M., Deng, Y., Jolly, M. K., Kaipparettu, B. A., et al. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01385-y · Cited by 97
The SuBliMinaL Toolbox: automating steps in the reconstruction of metabolic networks
Swainston, N., B, K. S. A., Mendes, P., Kell, D. B., & Paton, N. W. (2011) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2011-186 · Cited by 97
Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Su, M., Pan, T., Chen, Q.-Z., Zhou, W., Gong, Y., Xu, G., Yan, H.-Y., Li, S., Shi, Q.-Z., Zhang, Y., et al. (2022) · Military Medical Research · doi:10.1186/s40779-022-00434-8 · Cited by 114
Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions
Srinivasan, S., Jnana, A., & Murali, T. S. (2024) · Microbial Ecology · doi:10.1007/s00248-024-02370-7 · Cited by 96
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
Self-citationDynamic models of metabolism: Review of the cybernetic approach
Ramkrishna, D., & Song, H. (2012) · AIChE Journal · doi:10.1002/aic.13734 · Cited by 95
Biofuel production: an odyssey from metabolic engineering to fermentation scale-up
Hollinshead, W. D., He, L., & Tang, Y. (2014) · Frontiers in Microbiology · doi:10.3389/fmicb.2014.00344 · Cited by 95
Dynamic optimization of metabolic networks coupled with gene expression
Waldherr, S., Oyarzún, D. A., & Bockmayr, A. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.10.035 · Cited by 102
Emergent Biosynthetic Capacity in Simple Microbial Communities
Chiu, H.-C., Levy, R., & Borenstein, E. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003695 · Cited by 98
D’hoe, K., Vet, S., Faust, K., Moens, F., Falony, G., Gonze, D., Lloréns‐Rico, V., Gelens, L., Danckaert, J., Vuyst, L. D., et al. (2018) · eLife · doi:10.7554/elife.37090 · Cited by 90
A workflow for generating multi-strain genome-scale metabolic models of prokaryotes
Norsigian, C. J., Fang, X. D., Seif, Y., Monk, J. M., & Palsson, B. Ø. (2019) · Nature Protocols · doi:10.1038/s41596-019-0254-3 · Cited by 90
Self-citationCHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models
Haraldsdóttir, H. S., Cousins, B., Thiele, I., Fleming, R. M. T., & Vempala, S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx052 · Cited by 90
Self-citationLabhsetwar, P., Cole, J. A., Roberts, E., Price, N. D., & Luthey-Schulten, Z. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222569110 · Cited by 94
A review on computational systems biology of pathogen–host interactions
DurmuÅŸ, S., Çakır, T., Özgür, A., & Guthke, R. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00235 · Cited by 93
Generalized framework for context-specific metabolic model extraction methods
Robaina‐Estévez, S., & Nikoloski, Z. (2014) · Frontiers in Plant Science · doi:10.3389/fpls.2014.00491 · Cited by 91
Shrinking the Metabolic Solution Space Using Experimental Datasets
Reed, J. L. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002662 · Cited by 96
Pavan, M., Reinmets, K., Garg, S., Mueller, A. P., Marcellin, E., Köpke, M., & Valgepea, K. (2022) · Metabolic Engineering · doi:10.1016/j.ymben.2022.01.015 · Cited by 90
A Caenorhabditis elegans genome-scale metabolic network model
Yilmaz, L. S., & Walhout, A. J. M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.012 · Cited by 88
Stewart, P. S., White, B., Boegli, L., Hamerly, T., Williamson, K. S., Franklin, M. J., Bothner, B. B., James, G. A., Fisher, S. T., Vital-Lopez, F. G., et al. (2019) · Journal of Bacteriology · doi:10.1128/jb.00307-19 · Cited by 96
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth
Culley, C., Vijayakumar, S., Zampieri, G., & Angione, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002959117 · Cited by 94
Karp, P. D., Paley, S., Caspi, R., Kothari, A., Krummenacker, M., Midford, P. E., Moore, L. R., Subhraveti, P., Gama‐Castro, S., Tierrafría, V. H., et al. (2023) · EcoSal Plus · doi:10.1128/ecosalplus.esp-0002-2023 · Cited by 87
Pan, P., & Hua, Q. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051535 · Cited by 94
From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota
Bauer, E., & Thiele, I. (2018) · mSystems · doi:10.1128/msystems.00209-17 · Cited by 88
Self-citationA navigation guide of synthetic biology tools for Pseudomonas putida
Martín‐Pascual, M., Batianis, C., Bruinsma, L., Asin‐Garcia, E., Morales, L. G., Weusthuis, R. A., van Kranenburg, R., & dos Santos, V. A. P. M. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107732 · Cited by 92
Non-additive microbial community responses to environmental complexity
Pacheco, A. R., Osborne, M. L., & Segrè, D. (2021) · Nature Communications · doi:10.1038/s41467-021-22426-3 · Cited by 96
Heinken, A., Hertel, J., & Thiele, I. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00178-6 · Cited by 93
Self-citationDreyfuss, J. M., Zucker, J., Hood, H. M., Ocasio, L. R., Sachs, M. S., & Galagan, J. E. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003126 · Cited by 92
Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks
Prigent, S., Frioux, C., Dittami, S. M., Thiele, S., Larhlimi, A., Collet, G., Gutknecht, F., Got, J., Eveillard, D., Bourdon, J., et al. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005276 · Cited by 88
“As described in the third part of Fig 2, this definition excludes the self-production of a compound via cycles as it is allowed in mass-balanced stoichiometric frameworks: Meneco requires the independent production of all inputs in the cycle to “initiate” the reactions [28].”
The evolution of genome-scale models of cancer metabolism
Lewis, N. E., & Abdel-Haleem, A. M. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00237 · Cited by 87
Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test
Pinu, F. R., Granucci, N., Daniell, J., Han, T.-L., Carneiro, S., Rocha, I., Nielsen, J., & Villas-Bôas, S. G. (2018) · Metabolomics · doi:10.1007/s11306-018-1339-7 · Cited by 92
Román, M. S., & Wagner, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006340 · Cited by 88
The era of big data: Genome-scale modelling meets machine learning
Antonakoudis, A., Barbosa, R., Kotidis, P., & Kontoravdi, C. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.011 · Cited by 92
Inverse Optimization: Theory and Applications
Chan, T. C. Y., Mahmood, R., & Zhu, I. Y. (2023) · Operations Research · doi:10.1287/opre.2022.0382 · Cited by 89
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 93
Modelling microbial communities: Harnessing consortia for biotechnological applications
Ibrahim, M., Raajaraam, L., & Raman, K. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.048 · Cited by 91
Long-term phenotypic evolution of bacteria
Plata, G., Henry, C. S., & Vitkup, D. (2014) · Nature · doi:10.1038/nature13827 · Cited by 88
Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow
Brunk, E., George, K. W., Alonso-Gutiérrez, J., Thompson, M. G., Baidoo, E., Wang, G., Petzold, C. J., McCloskey, D., Monk, J. M., Yang, L., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.004 · Cited by 88
Self-citationSynthetic Lethality and Cancer – Penetrance as the Major Barrier
Ryan, C. J., Bajrami, I., & Lord, C. J. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.08.003 · Cited by 91
Nitrogen-use efficiency in maize (Zea mays L.): from ‘omics’ studies to metabolic modelling
Simons, M., Saha, R., Guillard, L., Clement, G., Armengaud, P., Canas, R., Maranas, C. D., Lea, P. J., & Hirel, B. (2014) · Journal of Experimental Botany · doi:10.1093/jxb/eru227 · Cited by 87
Advances in metabolic modeling of oleaginous microalgae
Tibocha-Bonilla, J. D., Zuñiga, C., Godoy-Silva, R. D., & Zengler, K. (2018) · Biotechnology for Biofuels · doi:10.1186/s13068-018-1244-3 · Cited by 84
Critical assessment of genome-scale metabolic networks: the need for a unified standard
Ravikrishnan, A., & Raman, K. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv003 · Cited by 87
Bauer, E., & Thiele, I. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0063-2 · Cited by 88
Self-citationAngione, C. (2019) · BioMed Research International · doi:10.1155/2019/8304260 · Cited by 96
Jenior, M. L., Moutinho, T. J., Dougherty, B. V., & Papin, J. A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007099 · Cited by 83
How Rhizobia Adapt to the Nodule Environment
Ledermann, R., Schulte, C. C. M., & Poole, P. S. (2021) · Journal of Bacteriology · doi:10.1128/jb.00539-20 · Cited by 87
Azer, K., Kaddi, C. D., Barrett, J. S., Bai, J. P. F., McQuade, S. T., Merrill, N. J., Piccoli, B., Neves, S. R., Marchetti, L., Lombardo, R., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.637999 · Cited by 87
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems
Yu, M., Kramer, M. H., Dutkowski, J., Srivas, R., Licon, K., Kreisberg, J. F., Ng, C. T., Krogan, N. J., Sharan, R., & Ideker, T. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.003 · Cited by 78
Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview
Sen, P., & Orešič, M. (2019) · Metabolites · doi:10.3390/metabo9020022 · Cited by 84
Constructing kinetic models of metabolism at genome‐scales: A review
Srinivasan, S., Cluett, W. R., & Mahadevan, R. (2015) · Biotechnology Journal · doi:10.1002/biot.201400522 · Cited by 88
Dotiwala, F., Santara, S. S., Binker-cosen, A. A., Li, B., Chandrasekaran, S., & Lieberman, J. (2017) · Cell · doi:10.1016/j.cell.2017.10.004 · Cited by 86
Choudhury, S., Moret, M., Salvy, P., Weilandt, D., Vassily Hatzimanikatis, A., & Mišković, L. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00519-y · Cited by 84
Kleeßen, S., Irgang, S., Klie, S., Giavalisco, P., & Nikoloski, Z. (2015) · The Plant Journal · doi:10.1111/tpj.12763 · Cited by 83
Methods for integration of transcriptomic data in genome-scale metabolic models
Kim, M. K., & Lun, D. S. (2014) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.08.009 · Cited by 89
Benedict, M. N., Mundy, M. B., Henry, C. S., Chia, N., & Price, N. D. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003882 · Cited by 87
Koehorst, J. J., van Dam, J. C. J., van Heck, R. G. A., Saccenti, E., dos Santos, V. A. P. M., Diez, M. S., & Schaap, P. J. (2016) · Scientific Reports · doi:10.1038/srep38699 · Cited by 84
Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica
Mishra, P., Lee, N., Lakshmanan, M., Kim, M., Kim, B., & Lee, D. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0542-5 · Cited by 84
Systematic design and in vitro validation of novel one-carbon assimilation pathways
Yang, X. N., Yuan, Q., Luo, H., Li, F., Mao, Y., Zhao, X., Du, J., Li, P., Ju, X., Zheng, Y., et al. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2019.09.001 · Cited by 84
Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast
Yu, R., Campbell, K., Pereira, R., Björkeroth, J., Qi, Q., Vorontsov, E. A., Sihlbom, C., & Nielsen, J. (2020) · Nature Communications · doi:10.1038/s41467-020-15749-0 · Cited by 83
Recent advances in machine learning applications in metabolic engineering
Patra, P., Disha, B. R., Kundu, P., Das, M., & Ghosh, A. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.108069 · Cited by 90
Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome
Kindschuh, W. F., Baldini, F., Liu, M. C., Gerson, K. D., Liao, J., Lee, H. H., Anton, L., Gajer, P., Ravel, J., Levy, M., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-022-01293-8 · Cited by 90
Benedict, M. N., Gonnerman, M. C., Metcalf, W. W., & Price, N. D. (2011) · Journal of Bacteriology · doi:10.1128/jb.06040-11 · Cited by 84
Zhang, J., Li, X., Olmedo, M., Holdorf, A. D., Shang, Y., Artal-Sanz, M., Yilmaz, L. S., & Walhout, A. J. M. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.07.010 · Cited by 77
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 88
Advances in flux balance analysis by integrating machine learning and mechanism-based models
Sahu, A., Blätke, M. A., Szymański, J., & Töpfer, N. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.004 · Cited by 86
The central role of the host cell in symbiotic nitrogen metabolism
Macdonald, S. J., Lin, G. G.-H., Russell, C. W., Thomas, G. H., & Douglas, A. E. (2012) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2012.0414 · Cited by 83
The principles of whole-cell modeling
Karr, J. R., Takahashi, K., & Funahashi, A. (2015) · Current Opinion in Microbiology · doi:10.1016/j.mib.2015.06.004 · Cited by 83
Cancer cells depend on environmental lipids for proliferation when electron acceptors are limited
Li, Z., Ji, B. W., Dixit, P. D., Tchourine, K., Lien, E. C., Hosios, A. M., Abbott, K. L., Rutter, J. C., Westermark, A. M., Gorodetsky, E. F., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00588-8 · Cited by 81
Pandey, V., Hadadi, N., & Vassily Hatzimanikatis, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007036 · Cited by 82
A biochemically-interpretable machine learning classifier for microbial GWAS
Kavvas, E. S., Yang, L., Monk, J. M., Heckmann, D., & Palsson, B. Ø. (2020) · Nature Communications · doi:10.1038/s41467-020-16310-9 · Cited by 85
Self-citationFluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways
Emwas, A.-H. M., Szczepski, K., Al-Younis, I. M., Lachowicz, J. I., & Jaremko, M. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.805782 · Cited by 83
Recent Progress in the Development of Metabolome Databases for Plant Systems Biology
Fukushima, A., & Kusano, M. (2013) · Frontiers in Plant Science · doi:10.3389/fpls.2013.00073 · Cited by 84
Manor, O., Levy, R., & Borenstein, E. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2014.07.021 · Cited by 84
Growth promotion and antibiotic induced metabolic shifts in the chicken gut microbiome
Plata, G., Baxter, N. T., Susanti, D., Volland, A., Gangaiah, D., Nagireddy, A., Mane, S. P., Balakuntla, J., Hawkins, T., & Mahajan, A. K. (2022) · Communications Biology · doi:10.1038/s42003-022-03239-6 · Cited by 80
Metabolic pathway analysis using stable isotopes in patients with cancer
Bartman, C., Faubert, B., Rabinowitz, J. D., & DeBerardinis, R. J. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00632-z · Cited by 93
Chu, S. H., Huang, M., Kelly, R. S., Benedetti, E., Siddiqui, J., Zeleznik, O. A., Pereira, A., Herrington, D. M., Wheelock, C. E., Krumsiek, J., et al. (2019) · Metabolites · doi:10.3390/metabo9060117 · Cited by 83
Berla, B. M., Saha, R., Maranas, C. D., & Pakrasi, H. B. (2015) · Scientific Reports · doi:10.1038/srep14894 · Cited by 76
Baroukh, C., Muñoz-Tamayo, R., Steyer, J.-P., & Bernard, O. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104499 · Cited by 80
Software platforms to facilitate reconstructing genome‐scale metabolic networks
Hamilton, J. J., & Reed, J. L. (2013) · Environmental Microbiology · doi:10.1111/1462-2920.12312 · Cited by 76
INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation
Filippo, M. D., Pescini, D., Galuzzi, B. G., Bonanomi, M., Gaglio, D., Mangano, E., Consolandi, C., Alberghina, L., Vanoni, M., & Damiani, C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009337 · Cited by 83
Comprehensive evaluation of the capacities of microbial cell factories
Kim, G. B., Kim, H. R., & Lee, S. Y. (2025) · Nature Communications · doi:10.1038/s41467-025-58227-1 · Cited by 82
de Lomana, A. L. G., Schäuble, S., Valenzuela, J. J., Imam, S., Carter, W., Bilgin, D. D., Yohn, C. B., Turkarslan, S., Reiss, D. J., Orellana, M. V., et al. (2015) · Biotechnology for Biofuels · doi:10.1186/s13068-015-0391-z · Cited by 83
Extending the biosynthetic repertoires of cyanobacteria and chloroplasts
Nielsen, A. Z., Mellor, S. B., Vavitsas, K., Włodarczyk, A., Gnanasekaran, T., de Jesus, M. P. R. H., King, B. C., Bakowski, K., & Jensen, P. E. (2016) · The Plant Journal · doi:10.1111/tpj.13173 · Cited by 79
Hale, V. L., Jeraldo, P., Mundy, M. B., Yao, J. Z., Keeney, G., Scott, N. E., Cheek, E. H., Davidson, J., Greene, M., Martinez, C., et al. (2018) · Methods · doi:10.1016/j.ymeth.2018.04.024 · Cited by 80
Hanemaaijer, M., Röling, W. F. M., Olivier, B. G., Khandelwal, R. A., Teusink, B., & Bruggeman, F. J. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00213 · Cited by 79
An algorithm for the reduction of genome-scale metabolic network models to meaningful core models
Erdrich, P., Steuer, R., & Klamt, S. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0191-x · Cited by 79
Decoding molecular interactions in microbial communities
Abreu, N. A., & Taga, M. E. (2016) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuw019 · Cited by 80
Metabolic Energy-Based Modelling Explains Product Yielding in Anaerobic Mixed Culture Fermentations
González‐Cabaleiro, R., Lema, J. M., & Rodríguez, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126739 · Cited by 85
Model‐driven design of a minimal medium for Akkermansia muciniphila confirms mucus adaptation
van der Ark, K. C. H., Aalvink, S., Diez, M. S., Schaap, P. J., de Vos, W. M., & Belzer, C. (2018) · Microbial Biotechnology · doi:10.1111/1751-7915.13033 · Cited by 79
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
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
