A mathematical model to guide genetic engineering of photosynthetic metabolism
Abstract The optimization of algae biomass productivity in industrial cultivation systems requires genetic improvement of wild type strains isolated from nature. One of the main factors affecting algae productivity is their efficiency in converting light into chemical energy and this has been a major target of recent genetic efforts. However, photosynthetic productivity in algae cultures depends on many environmental parameters, making the identification of advantageous genotypes complex and the achievement of concrete improvements slow. In this work, we developed a mathematical model to describe the key factors influencing algae photosynthetic productivity in a photobioreactor, using experimental measurements for the WT strain of Nannochloropsis gaditana . The model was then exploited to predict the effect of potential genetic modifications on algae performances in an industrial context, showing the ability to predict the productivity of mutants with specific photosynthetic phenotypes. These results show that a quantitative model can be exploited to identify the genetic modifications with the highest impact on productivity taking into full account the complex influence of environmental conditions, efficiently guiding engineering efforts. Highlights A mathematical model to predict algae growth in a photobioreactor was developed; The model predicts effects of alterations in photosynthetic metabolism; Model predictions were validated with experimental data from mutated strains; The model can identify priority targets for genetic engineering of photosynthesis.
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