Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraintbased 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 ␣-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.Biological systems are fundamentally complex, and thus a systems approach is necessary to account for the diversity of interactions that can occur among the myriad of molecular components that comprise living cells (1,5,14). The use of genome-scale metabolic reconstructions of an organism may prove to be a valuable tool in attempts to account for biological complexity and to elucidate the genotype-phenotype relationship. The annotation of full microbial genome sequences (2, 7) has enabled reconstruction of whole-cell metabolic networks (5,15,19,29). By using these reconstructed networks, detailed analyses of specific biological functions and system properties have been performed (11,12,21,25,27,31). In addition, numerous different in silico approaches have been developed and are available to analyze the properties of metabolic networks (11,16,24,28,34,36). While the rationales underlying the various methods are becoming widely accepted, there still has been limited prospective experimental verification of genome-scale in silico models with regard to their abilities to interpret and predict complex biological processes, such as adaptive evolution.In several studies the workers have productively combined computational and experimental approaches (4,17,32,33). In these studies, the in silico models were constructed and used to analyze specific metabolic subsystems accounting for ...