Large Scale Metabolic Characterization Using Flux Balance Analysis and Data Mining

Abstract

Genome-scale metabolic models of several microbes have been reconstructed from sequenced genomes in the last years. These have been used in several applications in Biotechnology and biological discovery, since they allow to predict the phenotype of the microorganism in distinct environmental or genetic conditions, using for instance Flux Balance Analysis (FBA). This work proposes an analysis workflow using a combination of FBA and Data Mining (DM) classification methods, aiming to characterize the metabolic behaviour of microorganisms using the available models. This framework allows the large scale comparison of the metabolism of different organisms and the prediction of gene expression patterns. Also, it can provide insights about transcriptional regulatory events leading to the predicted metabolic behaviour. DM techniques, namely decision tree and classification rules inference, are used to provide patterns of gene expression based on environmental conditions (presence/ absence of substrates in the media). The methods proposed are applied to the study of the metabolism of two related microbes: Escherichia coli and Salmonella typhimurium.