Three core applications, each targeting a specific phase where metabolic modeling reduces risk and avoids costly repeat runs.
Moving from 2L bioreactor to 100–500L pilot scale is the most common failure point in precision fermentation. kLa drops 3–10× and substrate gradients appear for the first time. Fermvyne pre-identifies the DO and feed rate corrections needed before the pilot run happens.
At 2,000–20,000L, hydrostatic pressure effects, longer mixing times, and impeller selection become dominant variables. A 30% titer drop at commercial scale can represent $200K+ per run. Fermvyne's commercial-scale module accounts for pressure depth correction and shear sensitivity.
When evaluating multiple strain candidates from your engineering pipeline, metabolic modeling predicts how each strain's flux phenotype will perform at scale — before you invest pilot run costs in candidates likely to fail due to overflow or oxygen sensitivity at higher density.
You run the bench bioreactors. Fermvyne gives you a prediction of how your current batch phenotype translates to the next scale — without needing to learn a new modeling language or wait for a modeling team.
You're accountable for scale-up timeline and cost. Fermvyne gives you a quantified risk view of each pilot run before you commit — and a documented rationale for process parameter choices.
You're engineering strains to overproduce. Fermvyne integrates your flux model outputs directly — you can import your own genome-scale model constraints and overlay them on Fermvyne's kLa and feed profiles.
Most teams start with a bench-to-pilot prediction for their current lead strain. The workflow takes under two hours from data upload to protocol output.