The U.S. Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) announced on May 15, 2023, that a new workflow developed by researchers at the Agile BioFoundry (ABF), a BETO-funded consortium of national laboratories and Agilent Research Laboratories (Agilent) addresses the need for faster analytical tools. According to BETO, the workflow “combines state-of-the-art analytical technologies with a machine learning-based algorithm, providing a faster and more powerful way to process data that could accelerate the Design-Build-Test-Learn framework, a bio-engineering cycle used to improve biomanufacturing research and processes.”

BETO notes that speeding up the bio-engineering cycle could ultimately speed up biomanufacturing research. According to BETO, one of the biggest barriers to accomplishing this is the ability to improve the Learn step of the cycle, which involves using data to improve future cycles. Improvements to the Learn step can happen only if large amounts of high-quality data are gathered in the Test step of the cycle, however.

BETO states that the consortium teams set out to create a workflow that could generate high-quality analytical Test data that could feed into the Learn step. The workflow they developed includes several components:

• A high-throughput analytical method developed in collaboration with Agilent that enables a threefold reduction in sample analysis time (compared to previous conventional approaches) by using optimized liquid chromatography conditions;
• The Automated Method Selection Software tool, which predicts the best liquid chromatography method to use for analyzing new molecules of interest; and
• PeakDecoder, a novel algorithm that processes multi-dimensional metabolite data and automatically calculates errors in metabolite identification.

To test the workflow’s effectiveness, the researchers used it to study metabolites of various strains of microorganisms engineered by ABF. The microorganisms they tested all have the capacity to make various bioproducts, such as polymer and diesel fuel precursors. According to BETO, using their workflow, the researchers were able to interpret 2,683 metabolite features across 116 microbial samples.

BETO states that the researchers see PeakDecoder “as a stepping stone towards creating an automated data-gathering pipeline.” According to BETO, the team is already working on leveraging state-of-the-art artificial intelligence methods like computer vision used in other fields. The next version of PeakDecoder is expected to have improved automation and identification performance and to be more applicable to other types of molecular profiling, including proteomics workflows.