Scaling microalgae cultivation: from environmental effects on cell growth to a predictive hybrid modeling framework

Microalgae are photosynthetic microorganisms that convert CO₂ into valuable biomass for nutrition, biofuel production, and wastewater treatment. However, scaling from laboratory to industrial volumes remains largely empirical, as cultivation conditions (light, nutrients, reactor geometry) create complex interactions that complicate bioprocess design. Here, we address this challenge through two complementary case studies. Large-scale experiments with the marine oleaginous microalga Nannochloropsis oculata were conducted across photobioreactor volumes from 1 to 285 L. Growth rates remained relatively stable across volumes, but biomass productivity varied significantly with scale and reactor geometry, indicating that simple volumetric extrapolation is insufficient for industrial design. To systematically explore condition-dependent scaling effects impractical to test at large scale, we used Chlamydomonas reinhardtii as a model organism. Cultures grown in microplates (200 µL) and flasks (50 mL) across 85 light–nutrient combinations, totalling over 1,300 growth curves, revealed that scaling factors are not constant: growth rate scaling is highly nonlinear, whereas biomass yield scales approximately linearly with nutrient availability. These observations motivated a hybrid modeling framework embedding bounded, biologically interpretable response surfaces into ODE growth models, allowing kinetic parameters to vary continuously with environmental factors. The model captures nonlinear light–nutrient interactions, predicts trajectories under untested conditions, and consistently outperforms classical mechanistic benchmarks. We further show that microplate- and flask-derived parameter surfaces are related through simple morphological transformations, suggesting that growth response structure is conserved across a 250-fold volume increase. This opens perspectives for high-throughput screening strategies where small-scale experiments directly inform larger-scale bioprocess design.