Chueh Loo Poh

The paper introduces a system for high-throughput in vivo DNA data encoding using bacterial conjugation and base editing, successfully encoding and retrieving binary data with high accuracy.

D2Sim is a computational simulator for Nanopore sequencing-based DNA data storage that improves error modeling and reduces sample difference deviations compared to existing tools.

This paper evaluates LLM capabilities for analyzing genome-scale metabolic models, identifying strengths and limitations across domain knowledge, flux prediction, pathway construction, and optimization tasks.

This paper introduces PUSDA, a framework combining ultra-high throughput sorting and NGS to generate large-scale sequence-function data, enabling ML-driven enzyme engineering with improved efficiency and productivity.

The paper develops a deep CNN-based system for automated microbial classification using colony images, achieving high accuracy in identifying five species.

This paper models and characterizes error sources in DNA data storage by analyzing sequence corruption and data-dependent errors to improve data recovery.

The paper develops tunable thermal-repressible split-T7 RNA polymerase systems for dynamic gene regulation using temperature as a control cue.

BMSS2 is a Python package for model selection and identifiability analysis in synthetic biology, offering tools for simulation, parameter inference, and sensitivity analysis through a database-driven framework.

The paper demonstrates how the SBOL standard can represent multicellular biological systems, enabling better sharing of complex designs across multiple cells.