BiGGR

BiGGR performs metabolic flux analysis and genome-scale metabolic reconstruction in R by using public reconstructions (BiGG, Recon2) and SBML to estimate flux distributions, quantify uncertainty, and predict expression-driven flux changes via Lsei-FBA.

Key Features:

• Integration with Public Databases: Uses public metabolic reconstructions including the BiGG database and Recon2 to assemble models from pathway, gene, and reaction queries.
• Flux Balance Analysis (FBA): Implements flux balance analysis using linear inverse modeling algorithms to estimate metabolic flux distributions by optimizing objective functions.
• Uncertainty Quantification: Quantifies uncertainty by sampling the constrained multidimensional flux space to generate ensembles of feasible flux configurations.
• Visualization: Produces hypergraph visualizations in which hyperedge widths are proportional to estimated flux values.
• Interoperability (SBML): Supports import and export of models encoded in Systems Biology Markup Language (SBML).
• Innovative Algorithm — Lsei-FBA: Implements Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression alterations.

Scientific Applications:

• Central carbon metabolism flux estimation: Calculates flux distributions for central carbon metabolism in healthy human brain tissue.
• Disease-associated metabolic shifts: Predicts metabolic flux changes associated with Alzheimer's disease using Lsei-FBA and compares predictions with independent measurements of cerebral metabolism.

Methodology:

Assembles models from public database queries (BiGG, Recon2) and SBML, estimates fluxes by flux balance analysis and linear inverse modeling, applies Lsei-FBA to predict expression-driven flux changes, samples the constrained multidimensional flux space for uncertainty quantification, and generates hypergraph visualizations with flux-proportional hyperedge widths.