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Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth
Abstract
Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.
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“Whereas small-scale cultivations and initial engineering steps are often performed in complex media in batch culture, during the scale-up of the process, cells are exposed to altered environmental conditions and environmental stress regimes [57,58].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [57]
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“Network reconstructions have been put to numerous uses over the last decade or so, including biological discovery [31], metabolic engineering [32,33], prediction of the outcome of adaptive evolution [34,35], network topology [36,37] and the assessment of phenotypic behaviour [38-41].”
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“…Balance Analysis (FBA), has been successfully applied to predict a range of biological phenomena such as gene knockout effects [1] and the evolutionary adaptation of microbial strains [2 – 4], and has been employed to predict drug targets [5] and to design microbial strains for bioengineering [6].”
Systems Biology of Industrial Microorganisms
Papini, M., Salazar, M. P., & Nielsen, J. (2010) · Springer eBooks · doi:10.1007/10_2009_59 · Cited by 27
Borchert, A. J., & Downs, D. M. (2017) · Journal of Bacteriology · doi:10.1128/jb.00140-17 · Cited by 27
REVIEW: Optimality models in the age of experimental evolution and genomics
Bull, J. J., & Wang, I. (2010) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2010.02054.x · Cited by 26
“…and that such experiments are even within the grasp of models incorporating the key experimental details References: 1(Refardt & Rainey, 2009); 2(Ibarra et al., 2002; Schuetz et al., 2007; Snitkin & Segre, 2008, Perez 2009); 3(Kerr et al., 2006; Eshelman et al., 2010; Wild et al., 2009). ª 2 0…”
BackgroundRecent Progress on the Analysis of Power-law Features in Complex Cellular Networks
Nacher, J. C., & Akutsu, T. (2007) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-007-0040-7 · Cited by 28
Dispensability of Escherichia coli ’s latent pathways
Cornelius, S. P., Lee, J. S., & Motter, A. E. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1009772108 · Cited by 26
From bit to it: How a complex metabolic network transforms information into living matter
Wagner, A. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-33 · Cited by 28
“For other organisms and more unusual environments [19-21] this does not always hold.”
Maximum entropy and population heterogeneity in continuous cell cultures
Fernández-de-Cossio-Díaz, J., & Mulet, R. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006823 · Cited by 28
“Since FBA has been a successful tool in the detection of relevant metabolic pathways in multiple organisms [48, 47, 63, 64, 65], we believe that this reduction improves the quality of our predictions.”
BackgroundPareto Optimality Explanation of the Glycolytic Alternatives in Nature
Ng, C. Y., Wang, L., Chowdhury, A., & Maranas, C. D. (2019) · Scientific Reports · doi:10.1038/s41598-019-38836-9 · Cited by 27
Dynamic Network Modeling of Stem Cell Metabolism
Shen, F., Cheek, C., & Chandrasekaran, S. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9224-9_14 · Cited by 26
Genome-Scale Investigation of the Metabolic Determinants Generating Bacterial Fastidious Growth
Gerlin, L., Cottret, L., Cesbron, S., Taghouti, G., Jacques, M., Genin, S., & Baroukh, C. (2020) · mSystems · doi:10.1128/msystems.00698-19 · Cited by 27
Parallel mapping of genotypes to phenotypes contributing to overall biological fitness
Gall, S., Lynch, M., Sandoval, N. R., & Gill, R. T. (2008) · Metabolic Engineering · doi:10.1016/j.ymben.2008.08.003 · Cited by 27
Systems biology of the human red blood cell
Jamshidi, N., & Palsson, B. Ø. (2006) · Blood Cells Molecules and Diseases · doi:10.1016/j.bcmd.2006.01.006 · Cited by 27
Self-citationAnderson, M. J., DeLaBarre, B., Raghunathan, A., Palsson, B. Ø., Brünger, A. T., & Quake, S. R. (2007) · Biochemistry · doi:10.1021/bi700096p · Cited by 27
“E. coliGKG230D DNA was obtained from previous adaptive evolution studies ( 1- 3 ) and cloned into a pETBlue-2 vector (Novagen) to introduce a C-terminal His6-Tag (2).”
MethodSelf-citationGene order constrains adaptation in bacteriophage T7
Springman, R., Badgett, M. R., Molineux, I. J., & Bull, J. J. (2005) · Virology · doi:10.1016/j.virol.2005.07.008 · Cited by 27
Bazurto, J. V., Farley, K. R., & Downs, D. M. (2016) · mBio · doi:10.1128/mbio.01840-15 · Cited by 26
Román, M. S., & Wagner, A. (2021) · Molecular Ecology · doi:10.1111/mec.16161 · Cited by 27
“…Mishra et al., 2018), and to explore questions in the field of evolution (Bajić et al., 2018; Barve & Wagner, 2013; William Harcombe et al., 2013; Ibarra et al., 2002; Notebaart et al., 2014; San Roman & Wagner, 2018, 2020; Sandberg et al., 2017) and ecology (Estrela, Sanchez-Gorostiaga et al.,…”
BackgroundMethodWerner, S., Diekert, G. B., & Schuster, S. (2010) · Journal of Molecular Evolution · doi:10.1007/s00239-010-9389-0 · Cited by 26
“This is relevant also in Flux Balance Analysis ( Ibarra et al. 2002; Schuetz et al. 2007; Schuster et al. 2008).”
BackgroundTegner, J. N., Skogsberg, J., & Björkegren, J. (2006) · Journal of Lipid Research · doi:10.1194/jlr.r600030-jlr200 · Cited by 26
“Indeed, models are being used for well-understood biological processes such as the cell cycle (57), cell growth (58), and metabolic analyses (59), and for comparative studies of the robustness of biological oscillation circuits (60).”
Metadata
- Authors
- Ibarra, R. U., Edwards, J. S., & Palsson, B. O.
- Affiliations
- University of California San Diego (US)
- University of Delaware (US)
- ORCID
- Edwards, Jeremy S.
- Venue
- Nature
- Published
- 2002-11
- Volume
- 420
- Issue
- 6912
- Pages
- 186-189
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2025663053
- Cited by
- 857
- References
- 23
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 22 · 2023: 21 · 2022: 17 · 2021: 27 · 2020: 23 · 2019: 29 · 2018: 34 · 2017: 42 · 2016: 44 · 2015: 34 · 2014: 46 · 2013: 41 · 2012: 51
- FWCI
- 12.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation
- U.S. Department of Energy
- Istituto Superiore di Sanità
- National Institutes of Health
- Biological and Environmental Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Escherichia coli K-12, adaptive evolution, optimal growth rate, glycerol metabolism, constraint-based metabolic modeling, in silico prediction, metabolic network evolution, carbon source utilization
- MeSH terms
- Adaptation, Physiological, Carbon, Computer Simulation, Escherichia coli, Biological Evolution, Glucose, Glycerol, Kinetics, Malates, Models, Biological, Phenotype, Selection, Genetic, Genome, Bacterial, Computational Biology, Acetic Acid, Succinic Acid, Genomics
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