In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annota ted genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, com putational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coil uses its metabolism to gro w at a maximal rate using the E. coil MG1655 metabolic reconstruction. Base d an this hypothesis, we formulated experiments that describe the quantitat ive relationship between a primary carbon source (acetate or succinate) upt ake rate, oxygen uptake rate, and maximal cellular growth rate. We found th at the experimental data were consistent with the stated hypothesis, namely that the E. coil metabolic network is optimized to maximize growth under t he experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial ce lls.