论文标题
通过增强学习的遗传切换开关的外部控制
External control of a genetic toggle switch via Reinforcement Learning
论文作者
论文摘要
我们研究了使用基于学习的策略通过外部控制方法稳定合成切换开关的问题。为了克服将使在合成生物学中实际使用的算法的数据效率问题，我们采用了SIM到真实的范式，通过在拨动开关的简化模型上通过培训来学习该策略，然后随后利用它来控制从In-Vivo In-Vivo In-Vivo实验中进行更真实模型的模型。我们的内部实验证实了该方法的生存能力，暗示其潜在的体内控制实现实现。
We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach. To overcome the data efficiency problem that would render the algorithm unfeasible for practical use in synthetic biology, we adopt a sim-to-real paradigm where the policy is learnt via training on a simplified model of the toggle switch and it is then subsequently exploited to control a more realistic model of the switch parameterized from in-vivo experiments. Our in-silico experiments confirm the viability of the approach suggesting its potential use for in-vivo control implementations.
