Engineered sensor bacteria evolve master-level gameplay through accelerated adaptation

Engineered sensor bacteria evolve master-level gameplay through accelerated adaptation
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工程传感器细菌通过加速适应进化出大师级的游戏玩法

DOI:
10.1101/2022.04.22.489191
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发表时间:
2022
期刊:
--
影响因子:
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通讯作者:
Prakash S
Prakash S
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作者:
Prakash S

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工程活细胞能够自己学习算法,例如玩棋盘游戏-人工智能的经典挑战-将允许复杂的生态系统和组织进行化学重新编程,以学习复杂的决策。然而,目前的基因电路编码决策算法未能实现自我编程,他们需要监督调整。我们展示了一种通过强化学习来改造基因电路的策略。我们创建了一个可扩展的通用库ofEscherichia coli菌株编码的基本适应性遗传系统能够持续调整其相对水平的表达,根据他们以前的行为。我们的品种可以学习掌握3×3棋盘游戏,如井字游戏,即使从一个完全无知的状态开始。我们提供了一个通用的遗传机制的自主学习的决策在多变的environments.One-Sentence SummaryWe提出了一个可扩展的策略,工程基因电路能够自主学习决策在复杂的环境中。
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 ofEscherichia colistrains 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.
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