Mathilde Koch

Large scale active-learning-guided exploration to maximize cell-free production

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

Reinforcement Learning for Bio-Retrosynthesis

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

Metabolic Perceptrons for Neural Computing in Biological Systems

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

Plug-and-Play Metabolic Transducers Expand the Chemical Detection Space of Cell-Free Biosensors

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

Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0

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.