Kieffer Coline, Genot Anthony J, Rondelez Yannick, Gines Guillaume
Laboratoire Gulliver, UMR 7083, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin, Paris, 75005, France.
LIMMS, CNRS-Institute of Industrial Science, IRL 2820, University of Tokyo, Tokyo, 153-8505, Japan.
Adv Biol (Weinh). 2023 Mar;7(3):e2200203. doi: 10.1002/adbi.202200203. Epub 2023 Jan 29.
DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.
在过去30年里,作为一种信息聚合物的DNA已逐渐成为合理构建具有强大信号处理能力的化学反应网络的关键分子。无论是受硅基世界的影响还是受自然计算的启发,分子编程在诊断应用中受到了关注。本综述特别感兴趣的是,分子分类器在疾病模式识别和样本分类方面已显示出有前景的结果。由于输入整合和计算都是在单个试管中于分子水平上进行的,这种低成本方法可能成为分子谱分析策略的一种补充工具,在分子谱分析策略中,所有生物标志物都使用高科技仪器独立进行定量分析。在介绍了分子分类器的基本组件之后,将讨论它们使用数字布尔逻辑或模拟神经网络架构的一些实验实现。