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

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  • Rafael U. Ibarra

    (University of California, San Diego)

  • Jeremy S. Edwards

    (University of Delaware)

  • Bernhard O. Palsson

    (University of California, San Diego)

Abstract

Annotated genome sequences1,2 can be used to reconstruct whole-cell metabolic networks3,4,5,6. These metabolic networks can be modelled and analysed (computed) to study complex biological functions7,8,9,10,11. In particular, constraints-based in silico models12 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 conditions13,14. 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.

Suggested Citation

  • Rafael U. Ibarra & Jeremy S. Edwards & Bernhard O. Palsson, 2002. "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth," Nature, Nature, vol. 420(6912), pages 186-189, November.
    • Handle: RePEc:nat:nature:v:420:y:2002:i:6912:d:10.1038_nature01149
    DOI: 10.1038/nature01149
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    Cited by:

    1. Iván Domenzain & Benjamín Sánchez & Mihail Anton & Eduard J. Kerkhoven & Aarón Millán-Oropeza & Céline Henry & Verena Siewers & John P. Morrissey & Nikolaus Sonnenschein & Jens Nielsen, 2022. "Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Markus J Herrgård & Stephen S Fong & Bernhard Ø Palsson, 2006. "Identification of Genome-Scale Metabolic Network Models Using Experimentally Measured Flux Profiles," PLOS Computational Biology, Public Library of Science, vol. 2(7), pages 1-11, July.
    3. Avraham E Mayo & Yaakov Setty & Seagull Shavit & Alon Zaslaver & Uri Alon, 2006. "Plasticity of the cis-Regulatory Input Function of a Gene," PLOS Biology, Public Library of Science, vol. 4(4), pages 1-1, March.
    4. Andras Gyorgy, 2023. "Competition and evolutionary selection among core regulatory motifs in gene expression control," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Lucia, Umberto, 2012. "Irreversibility in biophysical and biochemical engineering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 5997-6007.
    6. Matthew N Benedict & Michael B Mundy & Christopher S Henry & Nicholas Chia & Nathan D Price, 2014. "Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-14, October.
    7. Marcelo Rivas-Astroza & Raúl Conejeros, 2020. "Metabolic flux configuration determination using information entropy," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-19, December.
    8. Claudio Altafini & Giuseppe Facchetti, 2015. "Metabolic Adaptation Processes That Converge to Optimal Biomass Flux Distributions," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-13, September.
    9. Umberto Lucia & Giulia Grisolia, 2018. "Cyanobacteria and Microalgae : Thermoeconomic Considerations in Biofuel Production," Energies, MDPI, vol. 11(1), pages 1-16, January.
    10. William R Harcombe & Nigel F Delaney & Nicholas Leiby & Niels Klitgord & Christopher J Marx, 2013. "The Ability of Flux Balance Analysis to Predict Evolution of Central Metabolism Scales with the Initial Distance to the Optimum," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-11, June.

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