Biography
Jean-Baptiste Lugagne's research explores the development of cell-machine interfaces to precisely control and optimise biological processes for Engineering Biology applications. He holds a Master’s degree in Signal Processing Engineering and earned his PhD in Synthetic Biology from Université Sorbonne Paris Cité. He then conducted postdoctoral research at Boston University, where he focused on high-throughput single-cell control of gene expression. He joined the department in December 2024.
Most Recent Publications
Host-Aware Control of Gene Expression using Data-Enabled Predictive Control
Host-Aware Control of Gene Expression using Data-Enabled Predictive Control
Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering.
Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering.
Deep model predictive control of gene expression in thousands of single cells.
Deep model predictive control of gene expression in thousands of single cells.
Deep neural networks for predicting single cell responses and probability landscapes
Deep neural networks for predicting single cell responses and probability landscapes
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.
Research Interests
Jean-Baptiste Lugagne's group develops and uses advanced engineering solutions to study, design, and optimise biological systems for biomanufacturing and biomedical applications.
Key areas include:
- Systems and synthetic biology
- Biological control systems
- Biomanufacturing
- Smart microscopy
- Microfluidics
- Computer vision for biomedical data
Research Groups
Most Recent Publications
Host-Aware Control of Gene Expression using Data-Enabled Predictive Control
Host-Aware Control of Gene Expression using Data-Enabled Predictive Control
Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering.
Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering.
Deep model predictive control of gene expression in thousands of single cells.
Deep model predictive control of gene expression in thousands of single cells.
Deep neural networks for predicting single cell responses and probability landscapes
Deep neural networks for predicting single cell responses and probability landscapes
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.
