אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Reconstruction of genome-scale metabolic networks is considered as key step in quantifying the genotype-phenotype relationship. A major computational challenge involved in the reconstruction process is the identification of missing reactions in a metabolic network a process commonly referred to as gap-filling. Here, we present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE) that searches for missing reactions required to catalyze metabolic flux under steady-state whose presence is supported by various functional genomic data. The method follows a two-step procedure. First, functional genomics data is utilized to estimate the probability of including each reaction from a cross-species reactions database in the final reconstructed network. Then, metabolic flux analysis selects a set of high probability reactions, whose addition to the network would enable flux activation of all known reactions. The performance of MIRAGE, in comparison to previous methods, is demonstrated in the reconstruction of network models for E. coli and the cyanobacteria Synechocystis. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria, amenable for constraint-based modeling analysis and specifically for metabolic engineering. To demonstrate the utility of the reconstructed networks, a strain design method was applied to the reconstructed models to predict gene knockouts whose implementation is expected to significantly elevate the production rate of an important nutritional product, astaxanthin.