Scientists at the Joint BioEnergy Institute (JBEI) have unveiled groundbreaking methods that could significantly enhance the production of bio-based jet fuel. By engineering microbes more efficiently, they aim to reduce development timelines from years to mere weeks. According to Héctor García Martín, the director of Data Science and Modeling at JBEI, “If widely adopted, these approaches could reshape the industry.”
The research highlights two distinct methods. One utilizes artificial intelligence combined with lab automation to accelerate isoprenol production five-fold, while the other employs a genetic biosensor to achieve a remarkable 36-fold increase in fuel titers. This innovative approach enables scientists to test genetic designs 10 to 100 times faster than traditional methods.
The primary substance being targeted is isoprenol, which can be converted into DMCO, a synthetic jet fuel option that offers higher energy density than conventional petroleum-based fuels. As current battery technology lacks the necessary energy density for aviation, this biofuel alternative is critical for meeting future aviation demands.
Innovative Engineering Strategies
The research team has implemented two complementary engineering strategies to enhance bio-manufacturing efficiency. One method merges artificial intelligence with lab automation to expedite the testing and refining of genetic designs in biofuel-producing microbes. The second method focuses on transforming a microbe’s seemingly detrimental behavior into a valuable sensing tool, revealing previously unknown pathways that can enhance production.
In a significant advancement, a team led by Taek Soon Lee and Héctor García Martín developed an automated pipeline that minimizes reliance on human intuition in metabolic engineering. Utilizing robotics, the researchers can create and test hundreds of genetic designs simultaneously. They designed a custom microfluidic electroporation device capable of inserting genetic material into 384 Pseudomonas putida strains in under a minute—a task that typically requires hours when performed manually.
This rapid process enables a continuous learning cycle, where machine learning models analyze protein measurements and suggest optimal gene combinations through CRISPR interference. By completing six engineering cycles in just weeks, the team identified valuable genetic combinations that significantly increased fuel concentration.
Optimizing Microbial Systems for Production
Another team, led by Thomas Eng, focused on addressing a challenge faced by Pseudomonas putida: its tendency to consume the isoprenol it produces. They pinpointed two proteins that the microbe uses to detect the fuel and reconfigured this system into a biological biosensor. This innovative twist linked the sensor to essential survival genes, ensuring that only the microbes capable of producing high amounts of fuel would thrive.
This biosensing technique allowed the team to screen millions of microbial variants without the need for manual measurements. Their findings revealed that high-yielding strains sustain their production by adjusting their metabolism to utilize amino acids once glucose supplies are depleted.
As researchers work to transition these engineered microbial strains from laboratory environments to industrial fermentation systems, the potential for commercial application intensifies. The combination of depth-first AI optimization and breadth-first biosensor discovery provides a robust framework applicable to a range of bio-based products.
The implications of this research could be profound, paving the way for a new era of sustainable aviation fuel production. As industries seek greener alternatives, the advancements made at JBEI may play a pivotal role in meeting future energy demands while reducing environmental impact.
