BioMet Toolbox

BioMet Toolbox performs genome-scale analysis of metabolic networks and omics data using genome-scale models to support stoichiometric evaluation, gene-deletion simulations, and omics integration.

Key Features:

• Stoichiometric Analysis: Performs stoichiometric analysis using linear programming simulations to identify flux distributions and to optimize growth rates, substrate uptake rates, and metabolic production rates.
• Gene Deletion Simulations: Executes in silico single and double gene deletion simulations to assess gene essentiality and functional roles in metabolic pathways.
• Transcriptome and Interactome Integration: Integrates transcriptome data with interactome information to detect transcriptional changes concentrated around specific metabolites.
• High-Throughput In Silico Screening: Supports high-throughput in silico screening with fully standardized simulation procedures.
• Standardized Cross-Organism Simulations: Applies standardized simulations across model organisms, including fungi and bacteria, using genome-scale models.
• Gene Set and Microarray Analysis: Includes gene set analysis and basic microarray analysis to identify statistically significant gene sets and coregulated subnetwork structures within metabolic networks.

Scientific Applications:

• Metabolic Engineering: Supports design and evaluation of metabolic engineering strategies by predicting effects of genetic modifications on metabolic fluxes.
• Functional Genomics: Facilitates systematic analysis of gene function in metabolism through gene-deletion simulations and flux analysis.
• Phenotype Prediction: Enables prediction of metabolic capabilities and responses under varied conditions using genome-scale models and stoichiometric simulations.

Methodology:

Stoichiometric analysis via linear programming simulations to optimize growth, uptake, and production rates; in silico single and double gene deletion simulations; integration of transcriptome and interactome data to locate transcriptional changes around metabolites; high-throughput standardized simulation workflows across genome-scale models for fungi and bacteria; gene set analysis and microarray analysis for statistically significant gene sets and coregulated subnetworks.