2024-06-11-brancato24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
In vivo learning-based control of microbial populations density in bioreactors	A key problem in using microorganisms as bio-factories is achieving and maintaining cellular communities at the desired density and composition to efficiently convert their biomass into useful compounds. Bioreactors are promising technological platforms for the real-time, scalable control of cellular density. In this work, we developed a learning-based strategy to expand the range of available control algorithms capable of regulating the density of a single bacterial population in bioreactors. Specifically, we used a sim-to-real paradigm, where a simple mathematical model, calibrated using a single experiment, was adopted to generate synthetic data for training the controller. The resulting policy was then exhaustively tested in vivo using a low-cost bioreactor known as Chi.Bio, assessing performance and robustness. Additionally, we compared the performance with more traditional controllers (namely, a PI and an MPC), confirming that the learning-based controller exhibits similar performance in vivo. Our work demonstrates the viability of learning-based strategies for controlling cellular density in bioreactors, making a step forward toward their use in controlling the composition of microbial consortia.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	brancato24a	0	In vivo learning-based control of microbial populations density in bioreactors	941	953	941-953	941	false	Brancato, Sara Maria and Salzano, Davide and Lellis, Francesco De and Fiore, Davide and Russo, Giovanni and Bernardo, Mario di	given family Sara Maria Brancato given family Davide Salzano given family Francesco De Lellis given family Davide Fiore given family Giovanni Russo given family Mario di Bernardo	2024-06-11		Proceedings of the 6th Annual Learning for Dynamics & Control Conference	242	inproceedings	date-parts 2024 6 11	https://proceedings.mlr.press/v242/brancato24a/brancato24a.pdf