BioMet Toolbox: genome-wide analysis of metabolism

Abstract

The rapid progress of molecular biology tools for directed genetic modifications, accurate quantitative experimental approaches, high-throughput measurements, together with development of genome sequencing has made the foundation for a new area of metabolic engineering that is driven by metabolic models. Systematic analysis of biological processes by means of modelling and simulations has made the identification of metabolic networks and prediction of metabolic capabilities under different conditions possible. For facilitating such systemic analysis, we have developed the BioMet Toolbox, a web-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models. The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth rates, substrate uptake rates and metabolic production rates by detecting relevant fluxes, simulate single and double gene deletions or detect metabolites around which major transcriptional changes are concentrated. These tools can be used for high-throughput in silico screening and allows fully standardized simulations. Model files for various model organisms (fungi and bacteria) are included. Overall, the BioMet Toolbox serves as a valuable resource for exploring the capabilities of these metabolic networks. BioMet Toolbox is freely available at www.sysbio.se/BioMet/.

Figure 1.

System flow of the BioMet Toolbox. Central panel (gray color) represents required input files [GSMM from our repository, Metabolic Network file with interaction, Association files: Node-ORF and file with P-values (more detailed descriptions of input files can be found in the text)]. Alternative input files (dashed gray color) are allowed (Annotation for know interaction can replace Metabolic Network file with interaction and Node-ORF Association files; Transcriptome data analysis instead of file with P-values, and custom GSMM instead of models provided in the repository). Three applications with available sub-options (color-coded as corresponding application) are represented in rectangular boxes (Analysis methods) and example of the results (output files) in oval boxes: Reporter Features (orange), Reporter Subnetworks (blue) and BioOpt (green).