In comparison with intensive studies of genetic mechanisms related to biological evolutionary systems, much less analysis has been conducted on metabolic network responses to adaptive evolution that are directly associated with evolved metabolic phenotypes. Metabolic mechanisms involved in laboratory evolution of Escherichia coli on gluconeogenic carbon sources, such as lactate, were studied based on intracellular flux states determined from 13 C tracer experiments and 13 C-constrained flux analysis. At the end point of laboratory evolution, strains exhibited a more than doubling of the average growth rate and a 50% increase in the average biomass yield. Despite different evolutionary trajectories among parallel evolved populations, most improvements were obtained within the first 250 generations of evolution and were generally characterized by a significant increase in pathway capacity. Partitioning between gluconeogenic and pyruvate catabolic flux at the pyruvate node remained almost unchanged, while flux distributions around the key metabolites phosphoenolpyruvate, oxaloacetate, and acetyl-coenzyme A were relatively flexible over the course of evolution on lactate to meet energetic and anabolic demands during rapid growth on this gluconeogenic carbon substrate. There were no clear qualitative correlations between most transcriptional expression and metabolic flux changes, suggesting complex regulatory mechanisms at multiple levels of genetics and molecular biology. Moreover, higher fitness gains for cell growth on both evolutionary and alternative carbon sources were found for strains that adaptively evolved on gluconeogenic carbon sources compared to those that evolved on glucose. These results provide a novel systematic view of the mechanisms underlying microbial adaptation to growth on a gluconeogenic substrate.Biological systems are capable of adapting to environmental changes by invoking a number of different strategies to achieve optimal overall performance under a specific condition. Laboratory evolution methods have been used to understand adaptive changes in a wide variety of microorganisms (3,7,8,11,24). Whereas some evolved phenotypes, such as the growth rate or product secretion rate, are readily detectable, the underlying mechanisms that result in improved cellular properties during the evolutionary process are difficult to identify. A number of technologies are now available to help elucidate the underlying mechanistic changes by providing genome-wide measurements of adaptive changes in gene expression or genome sequence (7,11,18,19). While the molecular basis of evolution might be revealed by these technologies, they cannot directly account for the specific metabolic changes responsible for the improved phenotypes. More direct information on metabolic network responses to laboratory evolution is therefore necessary to understand changes in metabolic functions and to link possible genetic mechanisms of evolution to evolved metabolic phenotypes.The characteristics of metabolic networks can be dir...