Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA; Genetics and Genomics Academy, North Carolina State University, Raleigh, NC 27606, USA.
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA.
Curr Opin Biotechnol. 2023 Jun;81:102940. doi: 10.1016/j.copbio.2023.102940. Epub 2023 Apr 13.
The immense information density of DNA and its potential for massively parallelized computations, paired with rapidly expanding data production and storage needs, have fueled a renewed interest in DNA-based computation. Since the construction of the first DNA computing systems in the 1990s, the field has grown to encompass a diverse array of configurations. Simple enzymatic and hybridization reactions to solve small combinatorial problems transitioned to synthetic circuits mimicking gene regulatory networks and DNA-only logic circuits based on strand displacement cascades. These have formed the foundations of neural networks and diagnostic tools that aim to bring molecular computation to practical scales and applications. Considering these great leaps in system complexity as well as in the tools and technologies enabling them, a reassessment of the potential of such DNA computing systems is warranted.
DNA 具有巨大的信息密度和潜在的大规模并行计算能力，再加上数据生产和存储需求的迅速增长，这激发了人们对基于 DNA 的计算的重新兴趣。自上世纪 90 年代构建第一个 DNA 计算系统以来，该领域已经发展到包含各种不同的结构。从简单的酶促和杂交反应来解决小的组合问题，到模拟基因调控网络的合成电路，以及基于链置换级联的 DNA 逻辑电路，这些都为神经网络和诊断工具奠定了基础，旨在将分子计算应用于实际规模和应用中。考虑到系统复杂性以及支持它们的工具和技术的巨大飞跃，有必要重新评估这种 DNA 计算系统的潜力。
