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Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast

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  • Balázs Papp

    (University of Bath
    Collegium Budapest (Institute for Advanced Study) Szentháromság utca 2)

  • Csaba Pál

    (University of Bath
    Pázmány Péter Sétány 1/C)

  • Laurence D. Hurst

    (University of Bath)

Abstract

Under laboratory conditions 80% of yeast genes seem not to be essential for viability1. This raises the question of what the mechanistic basis for dispensability is, and whether it is the result of selection for buffering or an incidental side product. Here we analyse these issues using an in silico flux model2,3,4,5 of the yeast metabolic network. The model correctly predicts the knockout fitness effects in 88% of the genes studied4 and in vivo fluxes. Dispensable genes might be important, but under conditions not yet examined in the laboratory. Our model indicates that this is the dominant explanation for apparent dispensability, accounting for 37–68% of dispensable genes, whereas 15–28% of them are compensated by a duplicate, and only 4–17% are buffered by metabolic network flux reorganization. For over one-half of those not important under nutrient-rich conditions, we can predict conditions when they will be important. As expected, such condition-specific genes have a more restricted phylogenetic distribution. Gene duplicates catalysing the same reaction are not more common for indispensable reactions, suggesting that the reason for their retention is not to provide compensation. Instead their presence is better explained by selection for high enzymatic flux.

Suggested Citation

  • Balázs Papp & Csaba Pál & Laurence D. Hurst, 2004. "Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast," Nature, Nature, vol. 429(6992), pages 661-664, June.
  • Handle: RePEc:nat:nature:v:429:y:2004:i:6992:d:10.1038_nature02636
    DOI: 10.1038/nature02636
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    Cited by:

    1. João F Matias Rodrigues & Andreas Wagner, 2009. "Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-11, December.

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