Özet:
Prediction of phenotype from genome-scale high-throughput network and component information still remains a challenge for systems based approaches in biological research. The enhancement of our power of prediction of phenotype information would help unravel the complete quantitative genetic interaction network, which in turn would provide the link between genotype and phenotype, enabling to reach deductions about phenotype just with the knowledge of genotypic information on functional relationships and in return, this might set a milestone for the construction of quantitative genetic interaction networks of higher organisms including Homo sapiens as well as providing clues as to the open reading frames encoding human genetic disorders through the use of their homologues in a model organism. The aim of this thesis was to identify the effect of plasticity on the prediction of novel genetic interactions leading to a decrease in fitness and causing synthetic sickness or lethality within the network of yeast in a quantitative manner. In this study, systems-based information on various components and interaction levels was used for the prediction and identification of novel interactions leading to phenotypes. For this purpose, the yeast model organism was investigated under carefully controlled environments. The applicability of flux balance analysis for the prediction of epistasis was investigated in conjunction with the effect of biomass composition and environmental perturbations on metabolism in order to enhance the predictive power of metabolic flux analysis. Flux balance analysis was concluded to be suitable for the prediction of phenotypic information and genetic interactions through implementation of regulatory information and plasticity information provided from the response of organisms to environmental perturbations.
