Deep reinforcement learning for the control of microbial co-cultures in bioreactors.

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures within continuous bioreactors. We confirm that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities. Author summary In recent years, synthetic biology and industrial bioprocessing have been implementing increasingly complex systems composed of multiple, interacting microbial strains. This has many advantages over single culture systems, including enhanced modularization and the reduction of the metabolic burden imposed on strains. Despite these advantages, the control of multi-species communities (co-cultures) within bioreactors remains extremely challenging and this is the key reason why most industrial processing still uses single cultures. In this work, we apply recently developed methods from artificial intelligence, namely reinforcement learning combined with neural networks, which underlie many of the most recent successes of deep learning, to the control of multiple interacting species in a bioreactor. This approach is model-free-the details of the interacting populations do not need to be known-and is therefore widely applicable. We anticipate that artificial intelligence has a fundamental role to play in optimizing and controlling processes in synthetic biology.