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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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From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [57]
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“…Reed and Palsson 2004; Almaas et al. 2005; Nam et al. 2012) and, therefore, the evolutionary potential of changes in functional states and network expression across taxa (Westerhoff et al. 1984; Heinrich et al. 1991; Fell 1997; Edwards et al. 2001; Ibarra et al. 2002; Davidson and Erwin 2010).”
InfluentialYeast Genome-Scale Metabolic Models for Simulating Genotype–Phenotype Relations
Castillo, S., Patil, K. R., & Jouhten, P. (2019) · Progress in molecular and subcellular biology · doi:10.1007/978-3-030-13035-0_5 · Cited by 16
Evolutionary Systems Biology: Historical and Philosophical Perspectives on an Emerging Synthesis
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Duality, Thermodynamics, and the Linear Programming Problem in Constraint-Based Models of Metabolism
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Multi‐way analysis of flux distributions across multiple conditions
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“It is of interest in cellular systems biology to model the behavior of metabolism in the context of cell growth at genomescale [6–9].”
BackgroundOptimality Principles in the Regulation of Metabolic Networks
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“coli [23] as well as the lactic acid bacterium Lactobacillus plantarum [96] achieve optimal in silico predicted growth when adapted on the poor substrate glycerol by serial dilution.”
BackgroundMao, L., & Verwoerd, W. S. (2014) · Biotechnology for Biofuels · doi:10.1186/s13068-014-0118-6 · Cited by 16
“Briefly, a multi-objective equation comprises: 1) one objective that represents the maximization of the growth rate, which reflects the belief that microbes evolve to optimize their metabolisms for maximizing biomass production (growth rate) [43-46] and has been experimentally validated by numerous studies [44,47,48],…”
Source and regulation of flux variability in Escherichia coli
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“On the other hand, growth on glycerol is suboptimal but, when placed under selective pressure, the population undergoes adaptive evolution to reach the predicted optimal growth [3].”
BackgroundVelázquez, E., Álvarez, B., Fernández, L. Á., & de Lorenzo, V. (2022) · Microbial Biotechnology · doi:10.1111/1751-7915.14098 · Cited by 15
Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources
Woo, H., Kim, Y.-S., Kim, D., & Yoon, S. H. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00017-w · Cited by 14
“This is not surprising, considering that bacterial cellular metabolism has evolved to support cell growth; therefore, metabolic reactions are primarily bene fi cial for cell growth (Ibarra et al, 2002; Nielsen, 2007).”
BackgroundOptimal knockout strategies in genome-scale metabolic networks using particle swarm optimization
Nair, G., Jungreuthmayer, C., & Zanghellini, J. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1483-5 · Cited by 14
“Given that even in the face of genetic perturbations microorganisms redirect metabolic flux towards maximizing cellular growth [15], this high yield must be maintained at high growth rates.”
BackgroundLeveraging genome-scale metabolic models for human health applications
Chowdhury, S., & Fong, S. S. (2020) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2020.08.017 · Cited by 14
Lavoie, M., Saint‐Béat, B., Strauss, J., Guérin, S., Allard, A., Hardy, S. V., Falciatore, A., & Lavaud, J. (2020) · Biology · doi:10.3390/biology9020030 · Cited by 15
Analysis of Amino Acid Supplementation Effects on Hepatocyte Cultures Using Flux Balance Analysis
Yang, H., Roth, C. M., & Ierapetritou, M. (2011) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2010.0070 · Cited by 15
“as maximization of growth rate for a microorganism (Ibarra et al., 2002), or maximization of urea production for primary hepatocytes (Sharma et al.”
Biological systems modeling of metabolic and signaling networks
Wayman, J. A., & Varner, J. D. (2013) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2013.09.001 · Cited by 15
“Interestingly, similar to early cybernetic models, the most common metabolic objective function has been the optimization of biomass formation [15], although other metabolic objectives have also been estimated [16].”
BackgroundA system for studying evolution of life-like virtual organisms
Neyfakh, A. A., Baranova, N. N., & Mizrokhi, L. J. (2006) · Biology Direct · doi:10.1186/1745-6150-1-23 · Cited by 15
“Models of biochemical networks in microorganisms became trustable enough to make computational predictions about the paths of evolutionary adaptation of these networks to various sources of energy, carbon, nitrogen, etc. [16].”
BackgroundPerformance and robustness analysis reveals phenotypic trade-offs in yeast
Trivellin, C., Rugbjerg, P., & Lisbeth Olsson, A. (2023) · Life Science Alliance · doi:10.26508/lsa.202302215 · Cited by 17
“To the best of our knowledge, trade-offs between performance and robustness have been investigated mostly in single-phenotype and in silico studies (Ibarra et al, 2002; Stelling et al, 2002; Fischer & Sauer, 2005).”
BackgroundRestricted cooperative games on metabolic networks reveal functionally important reactions
Sajitz-Hermstein, M., & Nikoloski, Z. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.08.018 · Cited by 14
“A common choice for the objective function is the maximization of biomass production, which allows a wide range of predictions consistent with experimental observations for simple model organisms (Edwards and Palsson, 2000; Edwards et al., 2001; Ibarra et al., 2002).”
BackgroundBiogenesis of Medium-Chain-Length Polyhydroxyalkanoates
Kniewel, R., Lopez, O. R., & Prieto, M. A. (2019) · Springer eBooks · doi:10.1007/978-3-319-50430-8_29 · Cited by 14
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“Assuming a steady-state condition, MFA provides a flux distribution as the solution of the mass balance equation without the need for rate equations and kinetic parameters [20,21].”
BackgroundMethodPalsson, B. Ø. (2011) · Microbe Magazine · doi:10.1128/microbe.6.69.1 · Cited by 14
Self-citation
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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