Flux balance analysis of a genome-scale yeast model constrained by exometabolomic data allows metabolic system identification of genetically different strains.
A systems approach to biology requires a principled approach to pathway identification. In this study, the two nuclear petite yeast mutants K1Deltapet191a and K1Deltapet191ab and their parental industrial strain K1 were cultured in glucose-containing microaerobic chemostats. Exometabolomic profiles were used to infer the differences in the fermentation characteristics and respiration capacity of the strains. The ability of the metabolite measurement information to describe genetically different strains was investigated using a genome-scale yeast model. Flux balance analysis (FBA) of the model reveals that the objective function of minimal oxygen consumption enables the identification of the effect of genotypic differences when combined with the knowledge of the extracellular state of metabolism. The predicted decrease in oxygen consumption flux of K1Deltapet191a and K1Deltapet191ab strains with respect to the parental strain is about 80% and 100%, respectively, which coincides with the respiratory deficiencies of the strains. The expected increase in ethanol production rates in response to the decrease in the respiratory capacity was also predicted to be very close to the experimental values. This study shows the predictive power of the integrated analysis of genome-scale models with exometabolomic profiles, since accurate predictions could be made without any information about the respiration capacity of the strains. The FBA approach thereby enables identification of responsive pathways and so permits the elucidation of the genetic characteristics of strains in terms of expressed metabolite profiles.
Cakir
Tunahan
T
Department of Chemical Engineering, Bogaziçi University, 34342 Bebek, Istanbul, Turkey.
Efe
Cagri
C
Dikicioglu
Duygu
D
Hortaçsu
Amable
A
Kirdar
Betül
B
Oliver
Stephen G
SG
D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D014644:Genetic Variation / Q000235:genetics; D008954:Models, Biological; D020543:Proteome / Q000502:physiology*; D012441:Saccharomyces cerevisiae / Q000145:classification* / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*; D013045:Species Specificity
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins