[2S03a] 07. Biocatalysis Engineering for Sustainable Bioprocessing - Part 2

2:00 PM - 2:40 PM JST(5:00 AM - 5:40 AM UTC)

[2Biocat-06-KL] Toward Automated DBTL Workflows for Biocatalyst Development in a Biofoundry

○Seung-Goo Lee^1,2,3 (1. Korea Biofoundry (Korea), 2. Korea Research Institute of Bioscience and Biotechnology (KRIBB) (Korea), 3. University of Science and Technology (UST) (Korea))

Keywords:

biofoundry,synthetic biology,genetic circuits,enzyme engineering

Enzyme discovery and optimization remain central challenges in biocatalyst development for the bio-based economy. One of the most effective approaches for addressing this challenge is the exploration of designed libraries within a biofoundry framework. To this end, we developed a transcription factor–based screening system comprising quantitative and tunable genetic circuits and various reporter cells. The genetic enzyme screening system (GESS) enables high-throughput acquisition of activity-linked data at the single-cell level using flow cytometry, thereby facilitating genotype–phenotype coupling within Design–Build–Test–Learn (DBTL) workflows.The GESS system has been applied to the identification of metagenome-derived glycosidases and esterases, the first identification of a caprolactam cyclase, and the screening of D-threonine amidase. It was also used to engineer the regioselectivity of penicillin G amidase.In addition, our biofoundry platform was designed to support large-scale construction of genetic libraries. Recently, genetic libraries have been constructed in parallel using automated DNA assembly workflows within the biofoundry platform. This fabrication process was applied to the development of a bioconversion system that converts methane into isoprene, resulting in a significant acceleration in the development of target strains. All DBTL-based studies were conducted within the beta biofoundry at KRIBB, providing operational experience toward establishing a public biofoundry strategy. The national biofoundry initiative in Korea began in 2025.Future work aims to establish a fully AI-integrated DBTL cycle. In particular, GESS-based workflows are expected to generate structured, large-scale datasets, enabling data-driven and automated biocatalyst development within a next-generation biofoundry system.