Abstract
Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. 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. The reconstructed network models are supplied via standard SBML files.
Highlights
Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship [1]
The method aims to find missing reactions (from a universal database of candidate gap-filling reactions such as the Kyoto Encyclopedia of Genes and Genomes (KEGG)), supported by functional genomics data, whose addition to the network would lead to a functional model
The method follows a two-step procedure, starting with the utilization of functional genomics data to estimate the probability of including each reaction from the universal database in the reconstructed network, and metabolic flux analysis that selects the most likely set of reactions whose addition to the network would satisfy the above described objectives
Summary
Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship [1]. A modeling approach called constraint-based modeling serves to analyze the function of such networks by solely relying on simple physical-chemical constraints [15,16] and is frequently used to predict various phenotypes of microorganisms (reviewed in [3,17,18,19,20]). The two major computational challenges in metabolic network reconstruction are (i) the identification of missing reactions in a metabolic network, and (ii) the association of genes with network reactions. Others rely on an additional array of functional genomics data, including gene co-expression and protein-protein interactions [37,38,39,40,41,42,43]
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