Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabo... more Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.
In silico models of Escherichia coli metabolism have been developed to predict metabolic behavior... more In silico models of Escherichia coli metabolism have been developed to predict metabolic behavior and propose experimentally testable hypotheses. However, a thorough assessment of the metabolic phenotype requires well-designed experimentation and reproducible experimental techniques. A method for the quantitative analysis of E. coli metabolism in vivo within the framework of in silico phenotypic phase plane analysis is presented. Using this approach, we have quantitatively studied E. coli metabolism in various environmental conditions and nutritional media. Our experimental methodology, in combination with steady-state metabolic models, can be used to study biological properties and evaluate the metabolic capabilities of microbes.
Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabo... more Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.
In silico models of Escherichia coli metabolism have been developed to predict metabolic behavior... more In silico models of Escherichia coli metabolism have been developed to predict metabolic behavior and propose experimentally testable hypotheses. However, a thorough assessment of the metabolic phenotype requires well-designed experimentation and reproducible experimental techniques. A method for the quantitative analysis of E. coli metabolism in vivo within the framework of in silico phenotypic phase plane analysis is presented. Using this approach, we have quantitatively studied E. coli metabolism in various environmental conditions and nutritional media. Our experimental methodology, in combination with steady-state metabolic models, can be used to study biological properties and evaluate the metabolic capabilities of microbes.
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