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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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Cell Biology: Networks , Regulation and Pathways
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Cell Biology: Networks, Regulation and Pathways
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“Other analyses that can be performed on the metabolic model include pathway analysis [20], elementary flux mode analysis [21], gene expression analysis [22], and adaptive evolution analysis [23].”
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“It has been reported that bacterial gene regulation systems can be adapted in response to the environment without DNA mutation (Ibarra et al., 2002).”
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“The flux-balance approach has proven to be very successful in retrieving these fluxes [108-109].”
Background
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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