AI, Automation, Biosensors Propel Synthetic Jet Fuel

Researchers develop complementary strategies to rapidly engineer microbes that produce high-performance biofuel

Jan. 29, 2026 at 9:31pm

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Researchers at the Joint BioEnergy Institute (JBEI) have demonstrated two powerful strategies to dramatically speed up the process of engineering microbes that can ferment plant material into high-performance jet fuel. One approach combines artificial intelligence and lab automation to rapidly test and refine the genetic designs of biofuel-producing microbes, while the other turns a microbe's "bad habit" of consuming the fuel it produces into a powerful sensing tool that can uncover hidden pathways to boost production.

Why it matters

Producing sustainable, high-energy density jet fuel from renewable sources is a long-standing challenge in synthetic biology. These complementary strategies offer a blueprint for engineering microbes to make a wide range of bio-based products much faster than traditional trial-and-error methods, which could reshape the biomanufacturing industry.

The details

The two studies focus on engineering the bacterium Pseudomonas putida to produce isoprenol, a clear, volatile alcohol that can be converted into DMCO, a next-generation jet fuel. One team used automation and machine learning to systematically test hundreds of genetic designs in parallel, boosting isoprenol production five-fold in just a few weeks. The other team turned the microbe's natural fuel-sensing ability into a biosensor that could rapidly screen millions of variants and identify strains making up to 36 times more isoprenol. Together, these approaches offer a way to move much faster than traditional methods, going from years to months to develop new biofuel-producing microbes.

  • The two studies were recently published in Nature Communications and Science Advances.

The players

Thomas Eng

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

Taek Soon Lee

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

Héctor García Martín

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

David Carruthers

A scientific engineering associate with JBEI and BSE who developed the robotic workflow.

Aindrila Mukhopadhyay

BSE deputy director for science, director of Host Engineering at JBEI, and a coauthor 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

“Standard metabolic engineering is slow because you're relying on human intuition and biological knowledge. Our goal was to make strain improvement systematic and fast.”

— Héctor García Martín, Director of Data Science and Modeling at JBEI

“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

“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

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're also adapting their approaches to other microbes and target molecules, aiming to make them broadly applicable in biomanufacturing.

The takeaway

These complementary strategies of data-driven optimization and discovery-based biosensor screening offer a powerful blueprint for rapidly engineering microbes to produce a wide range of bio-based products, including sustainable jet fuel, much faster than traditional methods. If widely adopted, these approaches could dramatically accelerate the development of new biomanufacturing capabilities.