Speaker: Jean-Baptist Lugagne, PhD
Affiliation: Boston University
Position: Postdoctoral Researcher, Dunlop Lab
Hosted by: Gibson Lab

Biological systems are inherently dynamic, stochastic, and multi-dimensional. Traditionally, manipulating such systems in a predictable manner has been challenging, but advances in supervised machine learning have revolutionized fields like protein folding, drug discovery, and onsite diagnostics. Even more promising, recent developments in synthetic biology and laboratory automation have set the stage for more powerful AI frameworks that directly interact with biological processes. These frameworks can not only lead to significant improvements in learning efficiency but also enable real-time, precise control of complex biological systems. In this talk, I will discuss my work in developing interfaces that allow computers to interact with cells in real time. I will  first focus on how I used recent developments at the intersection of deep learning and control theory in combination with single-cell optogenetics to dynamically drive the expression of antibiotic resistance genes in thousands of E. coli cells, and revealed the impact of expression dynamics on growth and survival. I will also discuss recent work on using interactive frameworks to control multi-stable genetic circuits and growth in single cells. This research paves the way towards interactive AI platforms that actively seek new knowledge and solutions, and opens exciting avenues for fast and sophisticated control over pathological cellular processes, not only to better understand them but also as a potential treatment.

Research Link:
https://www.biorxiv.org/content/10.1101/2022.10.28.514305v1

Jean-Baptiste Lugagne earned a Bachelor’s in Electrical Engineering and a Master’s in Signal Processing Engineering from the Grenoble Instituts Nationaux Polytechniques. After discovering synthetic biology through the iGEM competition, he transitioned fields and obtained a PhD in Bioengineering from Université Sorbonne Paris Cité for his work on real-time control of a genetic toggle switch. Since 2018 he has been a post-doctoral researcher at Boston University in the group of Mary Dunlop, and has applied his expertise at the intersection of synthetic biology, machine learning, and control theory to studying antibiotic resistance dynamics and growth regulation in bacteria.

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