在不确定环境下通过强化学习进行基因电路的序列设计
Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
AI总结：
本文提出一种在不确定环境下优化基因电路的序列框架，结合模拟器模型和强化学习策略，有效处理生物分子反应的内在随机性和实验室条件的变异性。
摘要：
生物系统的设计受到生物分子反应内在随机性和实验室或实验条件变异性的不确定性的阻碍。本文提出了一种序列框架，用于在两种不确定性下优化基因电路。通过结合基于微分方程或马尔可夫跳跃过程的模拟器模型和强化学习（RL）策略，我们的方法建议实验以适应未知的实验室条件并考虑内在随机性。尽管以前的贝叶斯方法通过迭代的实验-推断-优化循环来处理不确定性，但它们通常需要每次实验轮次后计算成本高昂的推断和优化步骤，导致延迟。为克服这一瓶颈，我们提出了一种在可能的不确定参数分布上提前训练的简化方法。这种策略避免了在设计过程中显式参数推断的需要，使能够立即进行基于观察的适应。我们在异源基因表达模型和阻遏子电路模型上展示了我们的框架，证明其能够高效处理分子噪声和跨实验室变异性。
英文摘要：
The design of biological systems is hindered by uncertainty arising from both intrinsic stochasticity of biomolecular reactions and variability across laboratory or experimental conditions. In this work, we present a sequential framework to optimize genetic circuits under both forms of uncertainty. By employing simulator models based on differential equations or Markov jump processes alongside a reinforcement learning (RL) policy-based approach, our method suggests experiments that adapt to unknown laboratory conditions while accounting for inherent stochasticity. While previous Bayesian methods address uncertainty through iterative experiment-inference-optimization cycles, they typically require computationally expensive inference and optimization steps after each experimental round, leading to delays. To overcome this bottleneck, we propose an amortized approach trained up-front across a distribution of possible uncertain parameters. This strategy sidesteps the need for explicit parameter inference during the design cycle, enabling immediate, observation-based adaptation. We demonstrate our framework on models for heterologous gene expression and a repressilator circuit, showing that it efficiently handles both molecular noise and cross-laboratory variability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
