NanoFlux turns your existing bioreactor data into a working digital twin of your process. Test scenarios, predict scale-up, and de-risk transfers — before you commit to another wet-lab run.
Every failed bioreactor batch costs weeks of timeline and meaningful capital. Every process transfer carries risk. Every scale-up is its own experiment.
NanoFlux replaces guesswork with a calibrated digital model of your bioprocess. Fit it once with the data you already have. Then run thousands of scenarios in silico — before you touch a reactor.
Built by biotechnologists who understand the problem from the inside — not generic software vendors guessing at bioreactor dynamics. Affiliated with DTU Skylab and the Bio Innovation Institute.
NanoFlux Bio translates your bioreactor process into a calibrated ODE model — capturing cell growth, substrate consumption, product formation, dissolved oxygen, and pH dynamics in a single unified simulation.
Fit model parameters to your experimental data, compare culture modes side-by-side, and log sensor readings in a structured database — all from one interface.
From simulation to calibration, each module is designed around how bioprocess scientists actually work.
Each organism ships with a validated parameter library and family-specific ODE kinetics — no manual setup required. Library continues expanding as we onboard partner-specific cell lines.
Move the sliders. Watch the kinetic curves recompute in real time. This is a simplified Monod model — the full platform runs extended kinetics with substrate inhibition, product inhibition, and family-specific effects across 9 organisms.
A structured four-step process from configuration to calibrated model.
Built on proven scientific computing libraries, designed for reproducibility and extensibility.
from src.calibration import CalibrationEngine engine = CalibrationEngine() result = engine.fit( organism_id = "CHO", t_obs = [0, 24, 48, 72, 96], # hours X_obs = [0.1, 0.8, 2.1, 3.5, 3.2], # g/L biomass S_obs = [5.0, 4.2, 2.8, 0.9, 0.2], # g/L glucose params_to_fit = ["mu_max", "Ks", "Yxs"], ) print(result["rmse_before"]) # → 0.18 print(result["rmse_after"]) # → 0.04 (78% improvement) print(result["params_dict"]) # → {"mu_max": 0.041, "Ks": 0.15, "Yxs": 0.42}
A small team turning bench-side biotech experience into deployable digital infrastructure.
Advised by DTU faculty and Bio Innovation Institute mentors.
Send us a process spec. We'll come back with a calibrated model of your bioreactor, your organism, your conditions — in 7 days.
No spam. A real human will respond within 48 hours.
Or write directly to hello@nanofluxbio.com