Department of Computer Science, Duke University, Durham, NC, USA.
Nanoscale. 2023 May 4;15(17):7676-7694. doi: 10.1039/d2nr06202j.
DNA computing has emerged as a promising alternative to achieve programmable behaviors in chemistry by repurposing the nucleic acid molecules into chemical hardware upon which synthetic chemical programs can be executed. These chemical programs are capable of simulating diverse behaviors, including boolean logic computation, oscillations, and nanorobotics. Chemical environments such as the cell are marked by uncertainty and are prone to random fluctuations. For this reason, potential DNA-based molecular devices that aim to be deployed into such environments should be capable of adapting to the stochasticity inherent in them. In keeping with this goal, a new subfield has emerged within DNA computing, focusing on developing approaches that embed learning and inference into chemical reaction systems. If realized in biochemical contexts, such molecular machines can engender novel applications in fields such as biotechnology, synthetic biology, and medicine. Therefore, it would be beneficial to review how different ideas were conceived, how the progress has been so far, and what the emerging ideas are in this nascent field of 'molecular-scale learning'.
DNA 计算作为一种有前途的替代方法，通过将核酸分子重新用作可以执行合成化学程序的化学硬件，从而实现化学中的可编程行为。这些化学程序能够模拟多种行为，包括布尔逻辑计算、振荡和纳米机器人。细胞等化学环境的特点是不确定性，容易受到随机波动的影响。出于这个原因，旨在部署到这种环境中的潜在基于 DNA 的分子设备应该能够适应其中固有的随机性。为了实现这一目标，DNA 计算领域内出现了一个新的子领域，专注于开发将学习和推理嵌入化学反应系统的方法。如果在生化环境中实现，这种分子机器可以在生物技术、合成生物学和医学等领域产生新的应用。因此，回顾一下不同的想法是如何产生的，到目前为止进展如何，以及在这个新兴的“分子尺度学习”领域中出现了哪些新的想法，将是有益的。
