Dynamic flux balance analysis of diauxic growth in Escherichia coli
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Flux Balance Analysis (FBA) has been used in the past to analyze microbial metabolic networks. Typically, FBA is used to study the metabolic flux at a particular steady state of the system. However, there are many situations where the reprogramming of the metabolic network is important. Therefore, the dynamics of these metabolic networks have to be studied. In this paper, we have extended FBA to account for dynamics and present two different formulations for dynamic FBA. These two approaches were used in the analysis of diauxic growth in Escherichia coli. Dynamic FBA was used to simulate the batch growth of E. coli on glucose, and the predictions were found to qualitatively match experimental data. The dynamic FBA formalism was also used to study the sensitivity to the objective function. It was found that an instantaneous objective function resulted in better predictions than a terminal-type objective function. The constraints that govern the growth at different phases in the batch culture were also identified. Therefore, dynamic FBA provides a framework for analyzing the transience of metabolism due to metabolic reprogramming and for obtaining insights for the design of metabolic networks.
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Since COBRA approaches mainly operate at steady-state, dynamic frameworks will need to be adapted to account for dynamic fluctuations within the rumen environment.
“Moreover, since COBRA approaches mainly operates at steady-state, dynamic frameworks (Mahadevan et al., 2002; Baroukh et al., 2014) will need to be adapted to account for the dynamic fluctuations within the rumen environment.”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Mahadevan et al., 2002
Cites
Escherichia coli and Salmonella :cellular and molecular biology
Neidhardt, F. C. (1996) · ASM Press eBooks · Cited by 8,197 · see citing works
Systematic determination of genetic network architecture
Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J., & Church, G. M. (1999) · Nature Genetics · doi:10.1038/10343 · Cited by 2,509 · see citing works
Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network
Ideker, T., Thorsson, V., Ranish, J. A., Christmas, R., Buhler, J., Eng, J. K., Bumgarner, R., Goodlett, D. R., Aebersold, R., & Hood, L. (2001) · Science · doi:10.1126/science.292.5518.929 · Cited by 2,050 · 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
Varma, A., & Palsson, B. Ø. (1994) · Applied and Environmental Microbiology · doi:10.1128/aem.60.10.3724-3731.1994 · Cited by 1,058 · see citing works
Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use
Varma, A., & Palsson, B. Ø. (1994) · Nature Biotechnology · doi:10.1038/nbt1094-994 · Cited by 959 · see citing works
Edwards, J. S., Ibarra, R. U., & Palsson, B. Ø. (2001) · Nature Biotechnology · doi:10.1038/84379 · Cited by 941 · see citing works
Self-citationSchilling, C. H., Letscher, D., & Palsson, B. Ø. (2000) · Journal of Theoretical Biology · doi:10.1006/jtbi.2000.1073 · Cited by 664 · see citing works
Metabolic Fluxes and Metabolic Engineering
Stephanopoulos, G. N. (1999) · Metabolic Engineering · doi:10.1006/mben.1998.0101 · Cited by 576 · see citing works
Functional Genomics: Expression Analysis of Escherichia coli Growing on Minimal and Rich Media
Han, T., Bausch, C. L., Richmond, C. S., Blattner, F. R., & Conway, T. (1999) · Journal of Bacteriology · doi:10.1128/jb.181.20.6425-6440.1999 · Cited by 566 · see citing works
Cohen, B. A., Mitra, R. D., Hughes, J. D., & Church, G. M. (2000) · Nature Genetics · doi:10.1038/79896 · Cited by 533 · see citing works
On the optimization of differential‐algebraic process systems
Cuthrell, J. E., & Biegler, L. T. (1987) · AIChE Journal · doi:10.1002/aic.690330804 · Cited by 452 · see citing works
RNA expression analysis using a 30 base pair resolution Escherichia coli genome array
Selinger, D. W., Cheung, K. J., Mei, R., Johansson, E. M., Richmond, C. S., Blattner, F. R., Lockhart, D. J., & Church, G. M. (2000) · Nature Biotechnology · doi:10.1038/82367 · Cited by 353 · see citing works
Flux analysis of underdetermined metabolic networks: the quest for the missing constraints
Bonarius, H. P. J., Schmid, G., & TRAMPER, J. (1997) · Trends in biotechnology · doi:10.1016/s0167-7799(97)01067-6 · Cited by 332 · see citing works
Pramanik, J., & Keasling, J. D. (1997) · Biotechnology and Bioengineering · doi:10.1002/(sici)1097-0290(19971120)56:4 · Cited by 329 · see citing works
Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates
Varma, A., Boesch, B. W., & Palsson, B. O. (1993) · Applied and Environmental Microbiology · doi:10.1128/aem.59.8.2465-2473.1993 · Cited by 316 · see citing works
Acién, F. G., García-Camacho, F., Pérez, J. A. S., Fernández-Sevilla, J. M., & Grima, E. M. (1997) · Biotechnology and Bioengineering · doi:10.1002/(sici)1097-0290(19970905)55:5 · Cited by 248 · see citing works
High-Density Microarray-Mediated Gene Expression Profiling of Escherichia coli
Wei, Y. W., Lee, J.-M., Richmond, C. S., Blattner, F. R., Rafalski, J. A., & LaRossa, R. A. (2001) · Journal of Bacteriology · doi:10.1128/jb.183.2.545-556.2001 · Cited by 240 · see citing works
Acetate and Formate Stress: Opposite Responses in the Proteome of Escherichia coli
Kirkpatrick, C. A., Maurer, L. M., Oyelakin, N. E., Yoncheva, Y. N., Maurer, R., & Slonczewski, J. L. (2001) · Journal of Bacteriology · doi:10.1128/jb.183.21.6466-6477.2001 · Cited by 235 · see citing works
Bacterial transcript imaging by hybridization of total RNA to oligonucleotide arrays
de Saizieu, A., Certa, U., Warrington, J. A., et al. (1998) · Nature Biotechnology · doi:10.1038/nbt0198-45 · Cited by 226 · see citing works
Wong, P., Gladney, S., & Keasling, J. D. (1997) · Biotechnology Progress · doi:10.1021/bp970003o · Cited by 190 · see citing works
Gene Expression Profiling by DNA Microarrays and Metabolic Fluxes in Escherichia coli
Oh, M., & Liao, J. C. (2000) · Biotechnology Progress · doi:10.1021/bp000002n · Cited by 143 · see citing works
Biochemical systems theory and metabolic control theory: 1. fundamental similarities and differences
Savageau, M. A., Voit, E. O., & Irvine, D. H. (1987) · Mathematical Biosciences · doi:10.1016/0025-5564(87)90007-1 · Cited by 121 · see citing works
A cybernetic view of microbial growth: Modeling of cells as optimal strategists
Dhurjati, P., Ramkrishna, D., Flickinger, M. C., & Tsao, G. T. (1985) · Biotechnology and Bioengineering · doi:10.1002/bit.260270102 · Cited by 121 · see citing works
Tao, H., González, R., Martı́nez, A., Rodríguez, M. E., Ingram, L. O., Preston, J. F., & Shanmugam, K. T. (2001) · Journal of Bacteriology · doi:10.1128/jb.183.10.2979-2988.2001 · Cited by 118 · see citing works
Metabolic Engineering from a Cybernetic Perspective. 1. Theoretical Preliminaries
Varner, J. D., & Ramkrishna, D. (1999) · Biotechnology Progress · doi:10.1021/bp990017p · Cited by 92 · see citing works
Savageau, M. A., Voit, E. O., & Irvine, D. H. (1987) · Mathematical Biosciences · doi:10.1016/0025-5564(87)90008-3 · Cited by 88 · see citing works
Borth, N., Zeyda, M., & Katinger, H. W. D. (2000) · Biotechnology and Bioengineering · doi:10.1002/1097-0290(2000)71:4 · Cited by 86 · see citing works
Lendenmann, U., & Egli, T. W. (1998) · Biotechnology and Bioengineering · doi:10.1002/(sici)1097-0290(19980705)59:1 · Cited by 68 · see citing works
Large-scale prediction of phenotype: Concept
Varner, J. D. (2000) · Biotechnology and Bioengineering · doi:10.1002/1097-0290(20000920)69:6 · Cited by 55 · see citing works
Rédei, L. V. (1967) · Algebra · doi:10.1016/b978-1-4831-9761-6.50006-7 · Cited by 29 · see citing works
Giuseppin, M. L. F., & van Riel, N. A. W. (2000) · Metabolic Engineering · doi:10.1006/mben.1999.0134 · Cited by 28 · see citing works
van Riel, N. A. W., Giuseppin, M. L. F., & Verrips, C. T. (2000) · Metabolic Engineering · doi:10.1006/mben.1999.0137 · Cited by 19 · see citing works
Frederickson, R. (2000) · Nature Biotechnology · doi:10.1038/73665 · Cited by 16 · see citing works
Torres, N. V. (1994) · Biotechnology and Bioengineering · doi:10.1002/bit.260440306 · Cited by 13 · see citing works
Modeling of Escherichia coli growth and acetate formation under different operational conditions
Guardia, M. J., & García‐Calvo, E. (2001) · Enzyme and Microbial Technology · doi:10.1016/s0141-0229(01)00413-6 · Cited by 10 · see citing works
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Huws, S. A., Creevey, C. J., Oyama, L. B., Mizrahi, I., Denman, S. E., Popova, M., Muñoz-Tamayo, R., Forano, E., Waters, S. M., Hess, M., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02161 · Cited by 487 · see citing works
Self-citationSynthetic biology of cyanobacteria: unique challenges and opportunities
Berla, B. M., Saha, R., Immethun, C. M., Maranas, C. D., Moon, T. S., & Pakrasi, H. B. (2013) · Frontiers in Microbiology · doi:10.3389/fmicb.2013.00246 · Cited by 305 · see citing works
Optimization in computational systems biology
Banga, J. R. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-47 · Cited by 278 · see citing works
Reconstructing organisms in silico: genome-scale models and their emerging applications
Fang, X., Lloyd, C. J., & Palsson, B. O. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-00440-4 · Cited by 292 · see citing works
Mathematical models in microbial systems biology
Stelling, J. (2004) · Current Opinion in Microbiology · doi:10.1016/j.mib.2004.08.004 · Cited by 164 · see citing works
Flux-Balance Modeling of Plant Metabolism
Sweetlove, L. J., & Ratcliffe, R. G. (2011) · Frontiers in Plant Science · doi:10.3389/fpls.2011.00038 · Cited by 137 · see citing works
Metabolic network modeling of microbial communities
Biggs, M. B., Medlock, G. L., Kolling, G. L., & Papin, J. A. (2015) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1308 · Cited by 124 · see citing works
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 · 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
Baroukh, C., Muñoz-Tamayo, R., Steyer, J.-P., & Bernard, O. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104499 · Cited by 80 · see citing works
Genome scale models of yeast: towards standardized evaluation and consistent omic integration
Sánchez, B. J., & Nielsen, J. (2015) · Integrative Biology · doi:10.1039/c5ib00083a · Cited by 75 · see citing works
Bioreactor state estimation and control
Komives, C., & Parker, R. S. (2003) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2003.09.001 · Cited by 74 · see citing works
Spatiotemporal modeling of microbial metabolism
Chen, J., Gomez, J. A., Höffner, K., Phalak, P., Barton, P. I., & Henson, M. A. (2016) · BMC Systems Biology · doi:10.1186/s12918-016-0259-2 · Cited by 69 · see citing works
“The limitations of steady-state metabolic models have been addressed through dynamic extensions of stoichiometric models and classical FBA [4 – 7].”
MethodDoyle, F. J., & Stelling, J. (2006) · Journal of The Royal Society Interface · doi:10.1098/rsif.2006.0143 · Cited by 60 · see citing works
Self-citationMetabolic modeling of synthesis gas fermentation in bubble column reactors
Chen, J., Gomez, J. A., Höffner, K., Barton, P. I., & Henson, M. A. (2015) · Biotechnology for Biofuels · doi:10.1186/s13068-015-0272-5 · Cited by 61 · see citing works
A feed-forward loop guarantees robust behavior in <i>Escherichia coli</i> carbohydrate uptake
Kremling, A., Bettenbrock, K., & Gilles, E. D. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn010 · Cited by 45 · see citing works
Veith, N., Solheim, M., van Grinsven, K. W. A., Olivier, B. G., Levering, J., Grosseholz, R., Hugenholtz, J., Holo, H., Nes, I., Teusink, B., et al. (2015) · Applied and Environmental Microbiology · doi:10.1128/aem.03279-14 · Cited by 42 · see citing works
Ravindran, S., Hajinajaf, N., Kundu, P., Comes, J., Nielsen, D. R., Varman, A. M., & Ghosh, A. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00379 · Cited by 14 · see citing works
- Authors
- Mahadevan, R., Edwards, J. S., & Doyle, F. J.
- Affiliations
- University of Delaware (US)
- Corresponding author
- Edwards, Jeremy S.
- Venue
- Biophysical Journal
- Publisher
- Elsevier BV
- Published
- 2002-09
- Volume
- 83
- Issue
- 3
- Pages
- 1331-1340
- ISSN
- 0006-3495 (ISSN-L)
- PMC
- PMC1302231
- OpenAlex
- W2048596643
- Cited by
- 945
- References
- 37
- Citations by year
- 2026: 48 · 2025: 45 · 2024: 56 · 2023: 41 · 2022: 53 · 2021: 68 · 2020: 55 · 2019: 42 · 2018: 74 · 2017: 60 · 2016: 60 · 2015: 55 · 2014: 44 · 2013: 37 · 2012: 37
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Biophysical Journal (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- 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
- dynamic flux balance analysis, diauxic growth, Escherichia coli, metabolic network dynamics, metabolic reprogramming, batch culture, glucose metabolism, metabolic constraints
- MeSH terms
- Cell Division, Culture Media, Escherichia coli, Glucose, Metabolism, Models, Biological, Models, Theoretical, Oxygen, Time Factors
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