Prediction of the dynamic behavior of photoautotrophic growth of an oleaginous alga using a multiscale metabolic model

Background: The maximization of lipid productivity in microalgae is crucial for the biofuel industry and it can be achieved by manipulating their metabolism. However, little efforts have been made to apply metabolic models in a dynamic framework to predict possible outcomes to scenarios observed at an industrial scale. Here we present a dynamic framework for the simulation of large-scale photobioreactors. The framework was generated by merging together the genome-scale metabolic model of Chlorella vulgaris (iCZ843) with reactor-scale parameters, thus yielding a multiscale model. Results: We used a multiscale model to predict growth trends under different light intensities and nitrogen concentrations. Simulations of lipid accumulation quantified the trade-off between growth and lipid biosynthesis under nitrogen limitation. Moreover, our modeling approach quantitatively predicted the dependence of microalgal metabolism on light intensity and circadian oscillations. Finally, we used our model to design a reactor irradiance profile that maximized lipid accumulation, thus achieving a lipid productivity increase of 46% at a constant intensity of 966 μE m^(-2) s^(-1). Conclusions: Here we generated a dynamic framework that combines the modeling of phenomena at both the genome and reactor scale. This multiscale model was employed to predict the sensitivity of growth and composition variation of C. vulgaris on light and nitrogen levels, as well as to find a suitable irradiance profile that maximizes lipid productivity. Our modeling framework elucidated how metabolism and external factors can be combined to predict optimized parameters for industrial applications. Maestría