Cited by 857Microbial Metabolic Engineering and BioproductionEvolution and Genetic DynamicsGene Regulatory Network Analysis

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Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

Ibarra, Rafael U., Edwards, Jeremy S., Palsson, Bernhard O.·2002·Nature

DOI 10.1038/nature01149

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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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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
Publisher: Springer Science and Business Media LLC
Published: 2002-11
Volume: 420
Issue: 6912
Pages: 186-189
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature01149
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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Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

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