From CHO-based monoclonal antibodies to microbial precision fermentation, Augur predicts production-scale economics from minimal lab data. Each organism gets tailored kinetics, DSP templates, and cost modeling.
Predict production-scale mAb titer, COGS/kg, and DSP yield from bench-scale CHO cell culture runs.
Monoclonal antibody production is dominated by CHO cell culture, where scale-up from 2L to 2,000L+ bioreactors introduces unpredictable changes in dissolved oxygen, mixing, and cellular stress. Most teams discover their true production COGS only after committing to expensive manufacturing campaigns.
Augur's physics-informed neural networks predict CHO cell culture performance at production scale from as few as 5 bench-scale runs. The platform models kLa degradation, mixing time increase, and CO2 accumulation at scale — and couples these hydrodynamic changes with CHO-specific kinetics including product inhibition (Kp=8.0 g/L).
Model inclusion body formation, overflow metabolism, and refolding economics for recombinant protein production in E. coli.
E. coli fermentation at scale is fast and high-yielding but introduces challenges: acetate overflow metabolism above critical glucose uptake rates, inclusion body formation requiring expensive refolding steps, and heat generation that strains cooling systems. The DSP for inclusion body processing (solubilization, refolding, chromatography) often accounts for 70-80% of total COGS.
Augur models E. coli overflow metabolism (Crabtree effect) with acetate accumulation as a 6th ODE state variable. The platform predicts the critical glucose uptake rate (q_s_crit = 1.0 g/g/h) and models metabolite re-assimilation during diauxic shift. The integrated DSP pipeline includes extraction, precipitation, and refolding economics specific to inclusion body workflows.
Optimize secreted protein production in S. cerevisiae and Pichia pastoris with integrated ethanol overflow and DSP economics.
Precision fermentation using yeast expression systems is central to the bioeconomy — from food proteins to industrial enzymes. Saccharomyces cerevisiae produces ethanol via the Crabtree effect at high glucose concentrations, reducing biomass yield and product formation. Pichia pastoris avoids Crabtree but requires methanol induction, adding complexity. Both require organism-specific DSP templates and economics modeling.
Augur models ethanol overflow metabolism for S. cerevisiae (q_s_crit = 0.8 g/g/h, Y_overflow = 0.46 g/g) with metabolite tracking as a 6th ODE variable. For Pichia pastoris, the platform models AOX1-driven expression kinetics with temperature-dependent Arrhenius modeling (Ea = 55 kJ/mol, T_opt = 30°C). Both organisms include pre-configured DSP templates for secreted protein purification.
Predict production-scale economics for glycolipid biosurfactants — rhamnolipids in P. putida, sophorolipids in Starmerella bombicola, and mannosylerythritol lipids (MEL) in Moesziomyces aphidis. The only platform with foam fractionation DSP modeling + per-product-class economics.
Biosurfactants must compete on cost with petrochemical surfactants at $1–5/kg. Scale-up from lab to production (20,000–50,000L) changes everything: foaming becomes unmanageable, oxygen transfer degrades, and DSP costs can dominate total COGS. Most teams discover their true production economics only after committing capital. Traditional solvent extraction (ethyl acetate, ethanol) is expensive and unsustainable at scale. Meanwhile, foam fractionation — using the surfactant’s own surface activity for separation — is promising but poorly modeled. No existing simulation tool understands biosurfactant-specific DSP economics across all three product classes.
Augur supports all three major glycolipid classes with organism-specific kinetics and DSP templates: P. putida rhamnolipids (foam fractionation), S. bombicola sophorolipids (gravity phase separation, Holiferm-style), and M. aphidis MEL (solvent extraction with 95% solvent recovery built into the cost model). For sophorolipids, Augur models the gravity phase separation that makes DSP as low as 1% of total cost. For MEL, it accounts for industrial solvent recycling that cuts DSP cost 4× vs naïve single-pass estimates. Compare DSP strategies side by side — foam fractionation vs solvent extraction, or gravity separation vs crystallization — and see break-even pricing in the same view. For novel strains (engineered producers, wild-type isolates), the Custom organism flow lets you provide your own kinetics or let the model learn them from 5+ lab runs.
Scale-up prediction for P. putida and other microbial chassis producing organic acids, bioplastic precursors, and specialty chemicals.
Industrial biotechnology products compete on razor-thin margins against established petrochemical processes. Accurate COGS prediction at production scale is essential before committing capital, but scale-up data for novel chassis organisms like P. putida is scarce. Feedstock economics (glucose vs waste streams vs acetate) can make or break profitability, yet most simulation tools can’t model alternative carbon sources.
Augur provides P. putida kinetic profiles with organism-specific parameters and DSP templates optimized for small molecule recovery (extraction, crystallization, evaporation). The platform predicts production-scale COGS/kg with Monte Carlo confidence intervals, enabling go/no-go decisions based on economics rather than intuition. Scenario comparison lets you evaluate different feedstocks, scales, and DSP trains side by side.
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