Jean‐Baptiste Lugagne

An Open-Source 3D-Printed Vacuum Manifold for Automated DNA Isolation in the Opentrons Flex and OT-2

This paper introduces an open-source 3D-printed vacuum manifold for automated DNA isolation using Opentrons robots, reducing hands-on time and costs.

Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance

The paper investigates how heterogeneity in PhoP expression affects downstream gene activation and antimicrobial peptide tolerance in E. coli.

Control of a Bi-Stable Genetic System via Parallelized Reinforcement Learning

This paper uses reinforcement learning to control a bi-stable genetic system, showing improved accuracy and efficiency compared to existing methods.

Deep neural networks for predicting single cell responses and probability landscapes

This paper uses deep neural networks to predict single-cell responses and probability landscapes, showing they can infer cell dynamics despite noise and stochasticity.

Longitudinal single-cell chemical imaging of engineered strains reveals heterogeneity in fatty acid production

This study uses longitudinal single-cell chemical imaging to reveal fatty acid production heterogeneity in engineered E. coli strains over time.

Deep model predictive control of gene expression in thousands of single cells

This study uses deep learning to control gene expression dynamics in E. coli cells, enabling real-time, cell-specific regulation of gene expression patterns.

Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response

This study uses single-cell time-lapse microscopy to show that gene expression and growth dynamics in Escherichia coli influence survival during antibiotic stress.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

DeLTA 2.0 is a deep learning tool for analyzing time-lapse microscopy data, enabling accurate segmentation and tracking of single cells in two-dimensional environments.

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

The paper introduces a deep learning framework for automated cell segmentation, tracking, and lineage reconstruction in time-lapse microscopy data with high accuracy and speed.