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

Nature volume 420, pages 186–189 (2002)Cite this article

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.

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Figure 1: Growth of E. coli K-12 on malate.
Figure 2: Growth rate during adaptive evolution on glucose, malate, succinate and acetate.
Figure 3: Growth of E. coli K-12 on glucose.
Figure 4: Growth of E. coli K-12 on glycerol.

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Acknowledgements

We thank L. Ramos, J. DiTonno, S. Fong, J. Marciniak, N. Short and H. Bialy for technical assistance and for reviewing the manuscript. We acknowledge funding support from the National Institutes for Health and the National Science Foundation, and the Department of Energy Office of Biological and Environmental Research.

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Author notes

  1. Rafael U. Ibarra and Jeremy S. Edwards: These authors contributed equally to this work

Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California, 92093-0412, USA

    Rafael U. Ibarra & Bernhard O. Palsson

  2. Department of Chemical Engineering, University of Delaware, Delaware, 19716, Newark, USA

    Jeremy S. Edwards

Authors
  1. Rafael U. Ibarra
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  3. Bernhard O. Palsson
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Correspondence to Bernhard O. Palsson.

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We have filed a patent about the subject matter of this paper which has been licensed to Genomatica, Inc., a University of California at San Diego spin-off company. The University of California at San Diego, J.S.E. and B.Ø.P. hold interests in this company.

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Ibarra, R., Edwards, J. & Palsson, B. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002). https://doi.org/10.1038/nature01149

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