Scientists at Oak Ridge National Laboratory have created a platform that uses artificial intelligence to rapidly reprogram bacteria into efficient factories for producing valuable chemicals and materials. The breakthrough combines synthetic biology, AI, and statistical mapping to identify specific genetic triggers that control complex traits in microbes, supporting industrial biotechnology and domestic supply chains.

What Problem Does This AI-Powered Microbial Engineering Solve?

For decades, researchers have struggled with a fundamental challenge: understanding which genes control specific traits in bacteria. Traditional approaches required scientists to manually study the effects of gaining or losing entire genes, a slow and limited process. The new platform overcomes this by examining how tiny differences in DNA sequences affect bacterial function, enabling precise design of microbes with targeted capabilities.

The research team at Oak Ridge, working with collaborators, published their findings in Nature Communications. They leveraged a technique called quantitative trait locus (QTL) mapping, which had been used successfully in plants but rarely applied to bacteria because these microorganisms reproduce asexually with limited genetic variation.

How Did Researchers Create Genetically Diverse Bacterial Populations?

The key innovation involved reviving a 1970s-era technique called protoplast fusion. By fusing different Bacillus bacterial strains, researchers created large populations of genetically varied offspring called recombinants. This genetic diversity was essential for mapping which DNA variants control desired traits.

The team tested this approach across multiple bacterial species used in industrial biotechnology:

• Clostridium thermocellum: A bacterium that tolerates harsh industrial conditions and excels at breaking down and fermenting plant cellulose
• Novosphingobium aromaticivorans: A bacterium that breaks down aromatic compounds from plant lignin and converts them into high-value chemicals
• Stutzerimonas stutzeri: A versatile bacterium used in bioremediation and soil nutrient fixation to support plant growth and suppress pathogens

Researchers measured properties of the bacteria and identified DNA variants that explained differences in those traits. They then validated findings using CRISPR gene-editing tools to swap gene sections and confirm the effects.

How Automation and AI Accelerated the Research Process?

Creating genetically diverse bacterial populations created a new challenge: phenotyping, or measuring the characteristics of, thousands of individual organisms. The team solved this with automation and artificial intelligence. A robotic system precisely positioned bacterial culture plates so that high-resolution digital imaging could capture consistent data at the same angle and lighting. A computer vision model then processed the images and extracted trait information automatically.

"Unlike past approaches that study the effect of gaining or losing whole genes, the new approach lets us determine how small differences in the nucleotide sequence affect bacterial function," said Josh Michener, co-lead for the project and Biological Systems Design group leader at Oak Ridge National Laboratory.

Josh Michener, Biological Systems Design Group Leader at Oak Ridge National Laboratory

The automation delivered a dramatic speedup: phenotyping was accomplished 10 times faster than manual methods. This acceleration was critical because consistent data was essential for applying mathematical algorithms and computer vision models to extract meaningful patterns.

What Are the Real-World Applications of This Technology?

The platform enables discovery of specific genetic triggers for complex traits, supporting precise design of microbes with targeted capabilities. Practical applications include the breakdown and conversion of plant lignin into valuable products and the uptake of critical minerals. These capabilities support domestic production of valuable products and recovery of critical minerals and materials, enhancing the nation's supply chains and global competitiveness.

"We have now, for the first time ever, put all these pieces together for a platform that gets results on complex genes-to-traits linkages much faster," said Dan Jacobson, co-lead and computational systems biologist at Oak Ridge. "We built the genetically diverse bacteria population, identified DNA variants, and confirmed the work with gene editing."

Dan Jacobson, Computational Systems Biologist at Oak Ridge National Laboratory

The microbial QTL mapping platform is available for licensing at Oak Ridge National Laboratory. Scientists continue to deploy the method to study and engineer microbes for better manufacturing processes as part of the Department of Energy Center for Bioenergy Innovation. The platform is also being used to study plant-associated microbes as part of the DOE Secure Ecosystem Engineering and Design Science Focus Area, as well as by a program at Colorado State University studying airborne microbes.

Steps for Deploying AI-Driven Microbial Engineering in Industry

• Establish Genetic Diversity: Use protoplast fusion or alternative genome shuffling methods to create large populations of genetically varied microbial offspring that enable robust trait mapping
• Implement Automated Phenotyping: Deploy robotic systems and computer vision models to rapidly measure bacterial traits at scale, reducing manual labor and increasing data consistency
• Validate with Gene Editing: Use CRISPR or similar tools to confirm identified genetic variants by swapping gene sections and observing phenotypic changes in target organisms
• Scale to Multiple Microbe Species: Test the platform across different bacterial groups relevant to your industrial application, from cellulose-degrading species to aromatic compound converters

The project was supported primarily by the Oak Ridge Laboratory-Directed Research and Development program and the Center for Bioenergy Innovation. Additional funding came from the Secure Ecosystem Engineering and Design Science Focus Area and Michener's Department of Energy Early Career Research Award. Genome sequencing was conducted by the Joint Genome Institute, a Department of Energy Office of Science user facility.