Edward Vitkin, M.Sc. Thesis Seminar
Wednesday, 11.5.2011, 12:30
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.