Mathilde Koch

The paper uses active learning to explore a large combinatorial space of cell-free compositions to maximize protein production and identify critical parameters.

This paper introduces a reinforcement learning approach for bio-retrosynthesis to design metabolic pathways, validated on curated datasets and with media supplement suggestions.

The paper introduces metabolic perceptrons for binary classification of metabolite combinations using computational models and experimental validation in synthetic biological circuits.

This paper introduces hybrid cell-free biosensors that expand the range of detectable chemicals by combining synthetic metabolic cascades with transcription factor-based networks.

The paper demonstrates the use of RetroPath2.0 for molecular structure enumeration and virtual screening in chemical space, including applications for glass transition temperature prediction and antibacterial activity optimization.