A survey on molecular-scale learning systems with relevance to DNA computing
A survey on molecular-scale learning systems with relevance to DNA computing
复制标题
与 DNA 计算相关的分子级学习系统调查
DOI：
10.1039/d2nr06202j
复制
发表时间：
2023
期刊：
影响因子：
6.7
通讯作者：
Reif, John H.
中科院分区：
文献类型：
--
作者：
Nagipogu, Rajiv Teja;Fu, Daniel;Reif, John H.
DNA计算已经成为一种很有前途的替代方案，通过将核酸分子重新利用为可以执行合成化学程序的化学硬件来实现化学中的可编程行为。这些化学程序能够模拟不同的行为，包括布尔逻辑计算，振荡和纳米机器人。化学环境，如细胞，具有不确定性，易于发生随机波动。出于这个原因，旨在部署到这样的环境中的潜在的基于DNA的分子装置应该能够适应它们固有的随机性。为了实现这一目标，DNA计算中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。如果在生物化学背景下实现，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构思出来的，到目前为止进展如何，以及在这个新兴的“分子尺度学习”领域出现了什么新的想法，这将是有益的。
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’.