[3S00a] 01. ASBA - Part 8

3:05 PM - 3:20 PM JST(6:05 AM - 6:20 AM UTC)

[3ASBA-12-TA] Genetically encoded biosensors for advancing sustainable biomanufacturing

○Chueh Loo Poh^1,2,3,4 (1. Department of Biomedical Engineering, National University of Singapore (Singapore), 2. National Centre for Engineering Biology (Singapore), 3. NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (Singapore), 4. Department of Biochemistry, National University of Singapore (Singapore))

Keywords:

Biosensors,Biotechnology,Genetic Circuit designs,Synthetic Biology

Genetically encoded biosensors have emerged as powerful tools in biomanufacturing, addressing critical bottlenecks in biocatalyst/strain engineering and bioprocess control. Currently, the identification of engineered high-performance producer strains is hindered by the low throughput and high cost of standard analytical methods. To address these limitations, we are engineering biosensors that facilitate ultra-high-throughput sorting of producer cells via microfluidic systems, generating the large-scale datasets for machine learning/AI applications. Beyond screening, we are developing optogenetic biosensors to enable direct, precise, and dynamic control of gene expression for various applications including biomanufacturing, bio-patterning and DNA data storage. This presentation will focus on our work at the intersection of biosensing, microfluidics, and machine learning/AI. I will present our recent progress in integrating these ultra-high throughput biosensing platform with machine learning framework to accelerate biocatalyst engineering. I will also introduce our effort at NCEB in developing the foundations (e.g., data management in standardized formats) for ML/AI powered DBTL cycle. Finally, I will demonstrate how we are leveraging optogenetic biosensors in biomanufacturing to achieve time resolved gene activation in bioproduction.