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

TL;DR

This paper presents a learning-based control strategy for maintaining microbial population density in bioreactors, demonstrating its effectiveness and robustness through in vivo experiments and comparison with traditional controllers.

Contribution

It introduces a novel learning-based control approach for microbial density regulation in bioreactors using a sim-to-real paradigm, validated with in vivo experiments.

Findings

Learning-based controller performs comparably to traditional PI and MPC controllers.

The approach is robust and effective in vivo using low-cost bioreactors.

The method advances microbial community control for biotechnological applications.

Abstract

A key problem toward the use of microorganisms as bio-factories is reaching and maintaining cellular communities at a desired density and composition so that they can efficiently convert their biomass into useful compounds. Promising technological platforms for the real time, scalable control of cellular density are bioreactors. In this work, we developed a learning-based strategy to expand the toolbox of available control algorithms capable of regulating the density of a \textit{single} bacterial population in bioreactors. Specifically, we used a sim-to-real paradigm, where a simple mathematical model, calibrated using a few data, was adopted to generate synthetic data for the training of the controller. The resulting policy was then exhaustively tested in vivo using a low-cost bioreactor known as Chi.Bio, assessing performance and robustness. In addition, 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 showcases the viability of learning-based strategies for the control of cellular density in bioreactors, making a step forward toward their use for the control of the composition of microbial consortia.