On the 3rd of December, SKKU's Biofoundry Research Center, led by Professor Han Min Woo of the Department of Food Science and Biotechnology, has announced that they have established a new biofoundry system that maximizes the efficiency of laboratory automation by incorporating economic principles, thereby accelerating the development of technologies for producing high-value biobased materials.

This study has been widely recognized for going beyond simple robot-assisted automation by introducing an evaluation model that quantitatively analyzes cost and time efficiency, thereby opening a new paradigm in the field of biomanufacturing.

The research team integrated the Experiment Price Index (EPI), which enables an at-a-glance assessment of experimental economics, with the concept of Robot-Assisted Modules (RAMs) that can be assembled like Lego blocks. While conventional biofoundries have primarily focused on executing experiments using robotic automation, the team mathematically calculated the cost and time of each process to design optimal automation pathways that eliminate unnecessary steps and maximize performance.

Using this system, the research team established automated workflows for five core bioprocesses, including gene assembly and microbial genome editing, and applied them to the production of compounds with high industrial value. As a result, the team successfully achieved the rapid development of microbial strains capable of producing cannabigerolic acid (CBGA), a key component of medical cannabinoids, as well as the functional amino acid L-tryptophan. Notably, the system demonstrated markedly superior speed and accuracy compared with manual experimentation, thereby validating its potential for commercialization.

In addition, the research team presented a techno-economic analysis of biofoundry operations under large-scale project scenarios. The analysis provides concrete data showing that, with an annual utilization rate of 50–75%, the initial capital investment can be recovered within approximately five years, offering practical evidence to support investment decision-making by both industry and government.

This achievement is expected to serve as a foundation for future advancements toward a fully autonomous “self-driving laboratory,” in which artificial intelligence (AI) independently designs and executes experiments based on accumulated operational data.

This achievement is expected to serve as a foundation for future advancements toward a fully autonomous “self-driving laboratory,” in which artificial intelligence (AI) independently designs and executes experiments based on accumulated operational data.

This achievement is expected to serve as a foundation for future advancements toward a fully autonomous “self-driving laboratory,” in which artificial intelligence (AI) independently designs and executes experiments based on accumulated operational data.

Meanwhile, the research findings were published in the December 1 online edition of Trends in Biotechnology, a leading international journal in the field of biotechnology. This work was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, and other supporting organizations, including Daesang Corporation.

※ Title: Techno-economic assessment-guided biofoundry for microbial strain development

※ Journal: Trends in Biotechnology (Published by Cell Press; Impact Factor 14.9; top 1.4%)

※ DOI: https://doi.org/10.1016/j.tibtech.2025.11.002 

※ Authors: Han Min Woo, Professor (Corresponding Author, SKKU), Yu Been Heo, Ph.D. (First Author, SKKU)

※ Pure: https://pure.skku.edu/en/persons/han-min-woo/

※ Video Overview: https://youtu.be/5Yle6oRfBl0

▲ Automated workflow optimization development by the Experiment Price Index

and Techno-economic assessment of biofoundry