Berkeley Lab Teams Accelerate Synthetic Jet Fuel Production with AI and Biosensors

Researchers combine automation, machine learning, and engineered microbes to rapidly boost biofuel yields

Jan. 29, 2026 at 3:55pm

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Scientists at the Joint BioEnergy Institute (JBEI), managed by Lawrence Berkeley National Laboratory, have developed two complementary approaches to dramatically speed up the process of engineering microbes that can ferment plant material into high-performance jet fuels. One team used artificial intelligence and lab automation to rapidly test and refine the genetic designs of biofuel-producing microbes, while another turned a microbe's natural fuel-sensing ability into a powerful biosensor to uncover hidden pathways that boost production.

Why it matters

Producing sustainable synthetic jet fuel has been a long-standing challenge in biomanufacturing, as traditional methods are slow and expensive. These new approaches could reshape the industry, allowing small teams to develop new bioproducts in a fraction of the time it currently takes.

The details

The two studies, published in Nature Communications and Science Advances, focused on engineering the bacterium Pseudomonas putida to produce isoprenol, a clear, volatile alcohol that can be converted into a next-generation jet fuel. One team used an automated pipeline with machine learning to systematically test and optimize genetic designs, boosting isoprenol production five-fold. The other team rewired the microbe's natural fuel-sensing system into a biosensor, allowing them to rapidly screen millions of variants and identify strains that make up to 36 times more isoprenol.

  • The automation pipeline study was published in 2025.
  • The biosensor study was published in 2025.

The players

Joint BioEnergy Institute (JBEI)

A research institute managed by Lawrence Berkeley National Laboratory that focuses on developing biofuels and bioproducts.

Taek Soon Lee

Director of Pathway and Metabolic Engineering at JBEI and a staff scientist in Berkeley Lab's Biological Systems and Engineering Division.

Héctor García Martín

Director of Data Science and Modeling at JBEI and a staff scientist in Berkeley Lab's Biological Systems and Engineering Division.

Thomas Eng

JBEI deputy director of Host Engineering and a research scientist in Berkeley Lab's Biological Systems and Engineering Division.

Aindrila Mukhopadhyay

BSE deputy director for science, director of Host Engineering at JBEI, and a co-author on the biosensor study.

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What they’re saying

“These are two powerful complementary strategies. One is data-driven optimization; the other is discovery. Together, they give us a way to move much faster than traditional trial-and-error.”

— Thomas Eng, JBEI deputy director of Host Engineering and a research scientist in Berkeley Lab's Biological Systems and Engineering Division

“Our goal was to make strain improvement systematic and fast.”

— Héctor García Martín, Director of Data Science and Modeling at JBEI and a staff scientist in Berkeley Lab's Biological Systems and Engineering Division

“Automation didn't just make the experiments faster—it made the data cleaner. That clarity is what lets it uncover non-intuitive genetic combinations that we probably would have missed by hand.”

— Patrick Kinnunen, Former Berkeley Lab JBEI postdoctoral researcher

“What started as a frustrating bug became our biggest asset. We turned the microbe's fuel-eating behavior into a sensor that reports and selects for the best producers automatically.”

— Thomas Eng, JBEI deputy director of Host Engineering and a research scientist in Berkeley Lab's Biological Systems and Engineering Division

“If widely adopted, these approaches could reshape the industry. Instead of taking a decade and hundreds of people to develop one new bioproduct, small teams could do it in a year or less.”

— Héctor García Martín, Director of Data Science and Modeling at JBEI and a staff scientist in Berkeley Lab's Biological Systems and Engineering Division

What’s next

The teams are now working to scale their methods from lab experiments to industrially relevant fermentation systems, a critical step for producing synthetic aviation fuel at commercial levels. They are also adapting their approaches to other microbes and target molecules, aiming to make them broadly applicable in biomanufacturing.

The takeaway

These complementary approaches of data-driven optimization and discovery-based strain engineering are transforming the field of biomanufacturing, allowing researchers to develop new sustainable biofuels and bioproducts much faster than traditional methods.