FunGem

Genome-Scale Metabolic Model Generator

Welcome to FunGem

FunGem predicts TRY metrics — Titer, Rate, and Yield — for filamentous fungi in submerged fermentation, directly from your genome and bioreactor conditions. Upload a fungal genome, define your substrate and reactor parameters, and get an instant in-silico prediction to focus your wet lab effort. Any feedback is greatly appreciated.

1 Upload Genome
2 Define Media & Reactor
3 GEM Reconstruction
4 FBA Optimisation
5 TRY Prediction

Configure Your Run

Genome file (.faa / .fasta)
Organism name
Carbon Source
Carbon source
Concentration
Nitrogen Source
Nitrogen source
Concentration
C:N ratio ≈ 10 : 1 — typical range for filamentous fungi: 10:1 to 30:1
Advanced options
Inoculum size (% v/v)
Fermentation duration (h)
Phosphate source (optional)
Phosphate concentration

What GEMgen predicts

1
Taxon detection — reads your FASTA header, queries the BV-BRC taxonomy API to confirm the organism is fungal, and selects the appropriate reconstruction template
2
GEM reconstruction — uploads your genome to BV-BRC and triggers the ModelReconstruction app, which builds a genome-scale metabolic model (SBML) automatically
3
Model retrieval — fetches the completed SBML model from BV-BRC, stores it in the database, and passes it to the FBA layer along with organism-specific biological constants
4
Flux Balance Analysis — runs escher-FBA client-side on the reconstructed model, applying Michaelis-Menten kinetics, kLa O₂ constraints, C:N ratio, temperature & pH corrections, and maintenance energy
5
TRY output — returns Titer (g/L), Rate (g/L/h), and Yield (g biomass / g substrate) calibrated to your exact bioreactor conditions and inoculum
Assumptions: submerged liquid fermentation; no oxygen limitation (fully aerated); no solid-state or moisture-dependent processes. GEM reconstruction is performed by simplified locally coded scripts (BV-BRC ModelReconstruction will be used in future iterations - on private servers). Predictions are model-based estimates — experimental validation is always recommended.

Not sure where to start?

Try the pre-loaded example dataset: a proprietary filamentous fungi strain grown on corn steep liquor + glucose at 28 °C, 200 RPM. All TRY outputs are pre-computed so you can see exactly what to expect. Example ready