In Vivo Learning-Based Control of Microbial Populations Density in Bioreactors

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.