Prof Chris Barnes' lab publishes paper in PLoS Comp Biol

A recent paper published from Chris’ lab in PLoS Comp. Biol. by Neythen Treloar and Alex Fedorec 

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.

For more information on the work of the Barnes Lab please go to the website