Oct 30, 2018

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

BioRxiv : the Preprint Server for Biology
Neythen J TreloarChris P Barnes

Abstract

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity (in comparison with pure cultures) 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 - in the control of co-cultures within continuous bioreactors. We confirm that feedback via reinforcement learning can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform 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 promisi...Continue Reading

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Mentioned in this Paper

Metabolic Process, Cellular
Coculture Techniques
Environment
Nutrients
Intelligence
Psychological Reinforcement
Pharmacologic Substance
Species
Reinforcement Surgical Repair
Genetic Engineering

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