In vivo learning-based control of microbial populations density in bioreactors

Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo, Mario di Bernardo
Proceedings of the 6th Annual Learning for Dynamics & Control Conference, PMLR 242:941-953, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v242-brancato24a, title = {In vivo learning-based control of microbial populations density in bioreactors}, author = {Brancato, Sara Maria and Salzano, Davide and Lellis, Francesco De and Fiore, Davide and Russo, Giovanni and Bernardo, Mario di}, booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference}, pages = {941--953}, year = {2024}, editor = {Abate, Alessandro and Cannon, Mark and Margellos, Kostas and Papachristodoulou, Antonis}, volume = {242}, series = {Proceedings of Machine Learning Research}, month = {15--17 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v242/brancato24a/brancato24a.pdf}, url = {https://proceedings.mlr.press/v242/brancato24a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T In vivo learning-based control of microbial populations density in bioreactors %A Sara Maria Brancato %A Davide Salzano %A Francesco De Lellis %A Davide Fiore %A Giovanni Russo %A Mario di Bernardo %B Proceedings of the 6th Annual Learning for Dynamics & Control Conference %C Proceedings of Machine Learning Research %D 2024 %E Alessandro Abate %E Mark Cannon %E Kostas Margellos %E Antonis Papachristodoulou %F pmlr-v242-brancato24a %I PMLR %P 941--953 %U https://proceedings.mlr.press/v242/brancato24a.html %V 242 %X 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.
APA
Brancato, S.M., Salzano, D., Lellis, F.D., Fiore, D., Russo, G. & Bernardo, M.d.. (2024). In vivo learning-based control of microbial populations density in bioreactors. Proceedings of the 6th Annual Learning for Dynamics & Control Conference, in Proceedings of Machine Learning Research 242:941-953 Available from https://proceedings.mlr.press/v242/brancato24a.html.

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