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Flux Balance Analysis of a Genome‐Scale Yeast Model Constrained by Exometabolomic Data Allows Metabolic System Identification of Genetically Different Strains
Author(s) -
Çakır Tunahan,
Efe Çaǧrı,
Dikicioǧlu Duygu,
Hortaçsu Amable,
Kırdar Betül,
Oliver Stephen G.
Publication year - 2007
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp060272r
Subject(s) - flux balance analysis , yeast , fermentation , strain (injury) , biology , saccharomyces cerevisiae , mutant , genome , flux (metallurgy) , identification (biology) , genetics , metabolite , respiration , computational biology , gene , biological system , biochemistry , chemistry , botany , organic chemistry , anatomy
A systems approach to biology requires a principled approach to pathway identification. In this study, the two nuclear petite yeast mutants K1Δpet191a and K1Δpet191ab 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 K1Δpet191a and K1Δpet191ab 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.