Engineered sensor bacteria evolve master-level gameplay through accelerated adaptation

Abstract

The engineering of living cells able to learn algorithms by themselves, such as playing board games —a classic challenge for artificial intelligence— will allow complex ecosystems and tissues to be chemically reprogrammed to learn complex decisions. However, current engineered gene circuits encoding decision-making algorithms have failed to implement self-programmability and they require supervised tuning. We show a strategy for engineering gene circuits to rewire themselves by reinforcement learning. We created a scalable general-purpose library of Escherichia coli strains encoding elementary adaptive genetic systems capable of persistently adjusting their relative levels of expression according to their previous behavior. Our strains can learn the mastery of 3×3 board games such as tic-tac-toe, even when starting from a completely ignorant state. We provide a general genetic mechanism for the autonomous learning of decisions in changeable environments.One-Sentence SummaryWe propose a scalable strategy to engineer gene circuits capable of autonomously learning decision-making in complex environments.

Quantification of genetic variants in bacterial cultures by Sanger sequencing

“…Being able to predict such ratios allows for the early diagnosis of disease and the development of efficient personalised treatment procedures. Such examples of applications include using the ratios of methylated and unmethylated genes that are associated with the onset of cancer 3 as well as the quantification of mitochondrial heteroplasmy 5 . Genetic variant quantification has also been successfully used to determine the proportion of bacteria strains resistant to antimicrobial drugs in a multi-strain community 4 .…”