A biochemically structured predictive model of eukaryotic microalgal growth: Integrating radiative transfer, respiration, and pigment acclimation

Predictive modeling of phototrophic cultures in photobioreactors remains challenging because growth emerges from the coupling between radiative transfer, intracellular bioenergetics, and physiological acclimation. Although existing approaches have progressively integrated light attenuation and reactor‐scale heterogeneity, extending knowledge‐based formulations to eukaryotic microalgae still raises difficulties, especially regarding the role of respiration under illumination and the dynamic adjustment of pigment content. In this work, we establish a biochemically structured predictive model of photoautotrophic growth for the eukaryotic microalga Chlamydomonas reinhardtii . The model is built from an explicit stoichiometric decomposition of the main metabolic functions involved in biomass synthesis, pigment synthesis, photosynthetic energy conversion, respiration, and ATP‐consuming futile processes under redox regulation. Its structure is derived from intracellular conservation relationships and observability analysis, leading to a reduced formulation driven by two variables: the net conversion rate of photochemically productive photons within the biotic phase, , and a dissipative ATP sink. The kinetic formulation explicitly couples growth to radiative transfer and accounts for dynamic pigment acclimation through variable partitioning of biomass formation between residual biomass and pigments. Model parameters were either fixed from previous physiological and bioenergetic analyses or identified from batch‐culture experiments performed under different incident photon flux densities in a flat‐panel photobioreactor illuminated from one side. The model satisfactorily predicts biomass and pigment dynamics in batch and continuous cultures over a broad range of light conditions and dilution rates, while comparisons with oxygen‐exchange measurements under illumination provide additional support for its structural relevance. The proposed framework provides a predictive and mechanistically interpretable description of eukaryotic microalgal growth in photobioreactors.