Final Report Summary - BIOLEAP (Biotechnological optimization of light use efficiency in algae photobioreactors)
New renewable energy source are highly needed to compensate exhausting fossil fuels reserves and reduce greenhouse gases emissions. Some species of algae have an interesting potential as feedstock for the production of fuels, food and bio-based materials. Strong research efforts are however needed to fulfil this potential and address many issues involving optimization of cultivation systems, biomass harvesting and algae genetic improvement.
In this context the improvement of light use efficiency is seminal since the sun ultimately provides the energy supporting algae growth. Algae efficiency in converting solar radiation, however, depends on many environmental factors, including light intensity, temperature, nutrient and CO2 availability. Optimizing microalgae productivity in such a complex environment hinges on our ability to describe, in a quantitative manner, the effect of these various parameters as well as their mutual interactions. This was addressed in BioLEAP with the application of computational models that are capable of quantitative predictions that can prove especially useful in identifying which parameters have the largest impact on productivity.
This project also investigated how growing conditions affected algae metabolism using a combination of genomic, transcriptomic and metabolomics analyses. These evidenced how light availability in Nannochloropsis gaditana modulates the carbon partitioning and lipids biosynthesis.
All information obtained has been employed to isolate Nannochloropsis gaditana strains with genetic modifications that showed significant improvement in biomass productivity in industrially relevant conditions.
In this context the improvement of light use efficiency is seminal since the sun ultimately provides the energy supporting algae growth. Algae efficiency in converting solar radiation, however, depends on many environmental factors, including light intensity, temperature, nutrient and CO2 availability. Optimizing microalgae productivity in such a complex environment hinges on our ability to describe, in a quantitative manner, the effect of these various parameters as well as their mutual interactions. This was addressed in BioLEAP with the application of computational models that are capable of quantitative predictions that can prove especially useful in identifying which parameters have the largest impact on productivity.
This project also investigated how growing conditions affected algae metabolism using a combination of genomic, transcriptomic and metabolomics analyses. These evidenced how light availability in Nannochloropsis gaditana modulates the carbon partitioning and lipids biosynthesis.
All information obtained has been employed to isolate Nannochloropsis gaditana strains with genetic modifications that showed significant improvement in biomass productivity in industrially relevant conditions.