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.

翻译：将微生物用作生物工厂的关键问题在于，如何达到并维持细胞群落处于所需的密度和组成，使其能够高效地将生物质转化为有用的化合物。生物反应器是实现细胞密度实时、可扩展控制的有前景技术平台。在本研究中，我们开发了一种基于学习的策略，以扩展可用于调节生物反应器中*单一*细菌种群密度的现有控制算法工具库。具体而言，我们采用了从仿真到现实（sim-to-real）的范式，利用一个通过少量数据校准的简单数学模型，生成用于训练控制器的合成数据。随后，使用名为Chi.Bio的低成本生物反应器对所得到的控制策略进行了详尽的体内测试，评估其性能和鲁棒性。此外，我们还将其性能与传统控制器（即PI和MPC）进行了比较，证实基于学习的控制器在体内表现出相似的性能。我们的工作展示了基于学习的策略用于控制生物反应器中细胞密度的可行性，向将其用于控制微生物群落组成迈出了一步。