• DocumentCode
    2493168
  • Title

    Adaptive evolution of Esherichia coli towards a predicted phenotype

  • Author

    Fong, S.S. ; Palsson, B.O.

  • Author_Institution
    Dept. of Bioeng., Univ. of California, San Diego, CA, USA
  • Volume
    3
  • fYear
    2002
  • fDate
    23-26 Oct. 2002
  • Firstpage
    2215
  • Abstract
    With abundant genomic data now available, methods must be formulated to utilize this pool of data to yield meaningful biological information. Using a constraints-based approach, a genome-scale whole cell model has been constructed for Escherichia coli that can be used to predict behavioral characteristics of the K-12 MG1655 wild-type strain. In particular, phenotype phase-plane analysis can be used to distinguish different operating states of E. coli with particular interest in defining a line of optimal growth. A hypothesis was formulated that E. coli will naturally optimize its growth and exhibit a phenotype corresponding to the predicted line of optimality. Evolution experiments were carried out for 500 to 1000 generations where E. coli were allowed to naturally accumulate mutations. Various carbon sources with different entry points into central metabolism were used to test the robustness of the model. The experimental results showed that over the course of evolution, E. coli evolved towards the line of optimality and remained along the line of optimality while increasing its substrate uptake rate, oxygen uptake rate, and specific growth rate. Thus, the constraints-based model of E. coli serves as an accurate predictive tool in determining the growth behavior of E. coli under the parameters tested.
  • Keywords
    biochemistry; evolution (biological); genetics; microorganisms; physiological models; 30 C; 30 day; 37 C; E. coli; Esherichia coli; K-12 MG1655 wild-type strain; O2; adaptive evolution; behavioral characteristics; biological information; carbon sources; constraints-based approach; constraints-based model; genome-scale whole cell model; genomic data; growth behavior; in silico biology; line of optimality; mutations; optimal growth; oxygen uptake rate; phenotype phase-plane analysis; predicted phenotype; specific growth rate; substrate uptake rate; Biochemistry; Bioinformatics; Biological system modeling; Capacitive sensors; Evolution (biology); Genetic mutations; Genomics; Predictive models; Robustness; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
  • ISSN
    1094-687X
  • Print_ISBN
    0-7803-7612-9
  • Type

    conf

  • DOI
    10.1109/IEMBS.2002.1053247
  • Filename
    1053247