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Reinforcement learning optimizes genetic circuit design under uncertainty

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新的顺序框架，利用强化学习来优化基因电路的设计，以应对生物系统中固有的不确定性。该方法采用模拟器模型和一个预先训练好的摊销方法，以适应未知的实验室条件和分子噪声，从而避免了在每个实验步骤后进行计算密集型推理的需要。该框架已在基因表达和阻遏子电路模型上得到验证，显示出在处理随机性和跨实验室变异性方面的效率。 AI

影响 引入了一种新颖的基于RL的优化生物系统设计的方法，有可能加速合成生物学领域的研究。

排序理由 这是一篇发表在arXiv上的研究论文，详细介绍了一种设计基因电路的新方法。

在 arXiv cs.LG 阅读 →

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Reinforcement learning optimizes genetic circuit design under uncertainty

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Michal Kobiela, Diego A. Oyarz\'un, Michael U. Gutmann ·

    不确定性下基于强化学习的基因电路序贯设计

    arXiv:2605.06552v1 Announce Type: new Abstract: 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 fram…

  2. arXiv cs.LG TIER_1 English(EN) · Michael U. Gutmann ·

    不确定性下基于强化学习的基因电路序贯设计

    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 fo…

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