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

摘要: Annotated genome sequences1,2 can be used to reconstruct whole-cell metabolic networks3,4,5,6. These networks modelled and analysed (computed) study complex biological functions7,8,9,10,11. In particular, constraints-based in silico models12 have been calculate optimal growth rates on common carbon substrates, the results were found consistent with experimental data under many but not all conditions13,14. Optimal functions are acquired through an evolutionary process. Thus, incorrect predictions of models based performance criteria may due incomplete adaptive evolution conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally glycerol as sole source. Here we show that when placed selection pressure, rate E. reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value predicted model. open possibility using entire realize states determined priori analysis.