Since 2009, Synthetic Genomics and ExxonMobil have worked together to create high-density, cost-competitive transportation fuel from algae. On the heels of a major scientific breakthrough published in Nature Biotechnology in 2017, Exxon and Synthetic Genomics have recently set a goal to develop the technical ability to produce 10,000 barrels of algae biofuel per day by 2025.
Algae is an attractive source for biofuel because it produces energy-dense oil content, grows in salt water and thrives in harsh environmental conditions. However, finding a strain of algae that is oil rich while also growing rapidly — a formula to develop algae biofuels at commercial scale — has eluded scientists for a decade. Until recently.
Researchers at Synthetic Genomics discovered a genetic switch that could be fine-tuned to regulate the conversion of carbon to oil in the algae species, Nannochloropsis gaditana. The team established a proof-of-concept approach that doubled the algae’s lipid content while sustaining growth.
The path leading to that pivotal point began with a global algae-collection effort. The team collected thousands of diverse strains in search of a natural species with desired characteristics. To narrow down to Nannocholoropsis, researchers used advanced genomics to sequence the strain’s 9,000 genes and found 20 potential “master regulators” of lipid production. Using gene editing to knock out each gene individually, they found one — the ZynCys gene — that dramatically increased lipid production when removed, but also stunted growth. By applying a different tool, RNA inference, they were able to fine-tune expression of the ZynCys gene until algae grew at about the same rate as wild algae, but with more than double the lipid production.
While fundamental research on algae continues in the laboratory, the companies are conducting a new phase of outdoor field study with naturally occurring algae in several contained ponds in California. The research will enable ExxonMobil and Synthetic Genomics to better understand fundamental engineering parameters, which cannot easily be replicated in a lab. The results of this work are important to understand how to scale the technology for potential commercial deployment.