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Journal of Bacteriology, November 2003, p. 6400-6408, Vol. 185, No. 21
0021-9193/03/$08.00+0     DOI: 10.1128/JB.185.21.6400-6408.2003
Copyright © 2003, American Society for Microbiology. All Rights Reserved.

Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model

Stephen S. Fong, Jennifer Y. Marciniak, and Bernhard Ø. Palsson*

Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412

Received 28 February 2003/ Accepted 23 July 2003

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of {alpha}-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37°C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.

* Corresponding author. Mailing address: University of California, San Diego, 9500 Gilman Dr., Mail code 0412, La Jolla, CA 92093-0412. Phone: (858) 534-5668. Fax: (858) 822-3120. E-mail: palsson{at}ucsd.edu.

Journal of Bacteriology, November 2003, p. 6400-6408, Vol. 185, No. 21
0021-9193/03/$08.00+0     DOI: 10.1128/JB.185.21.6400-6408.2003
Copyright © 2003, American Society for Microbiology. All Rights Reserved.



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