Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner. This review discusses the role of DMF as a “digital bioconverter”—a tool to connect the digital aspects of the design–build–learn cycle with the physical execution of experiments. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.

数字微流体 (DMF) 是一种液体处理技术，已被证明可以以低成本、快速和可编程的方式自动化生物实验。本综述讨论了 DMF 作为“数字生物转换器”的作用——一种将设计-建造-学习周期的数字方面与实验的物理执行联系起来的工具。回顾了几个应用以证明 DMF 作为数字生物转换器的实用性，即基因工程、测序和质谱的样品制备，以及基于酶、免疫和细胞的筛选分析。这些应用表明，DMF 在用于生产新型有机体和生物分子的完全集成管道中，在集中执行平台的作用方面具有巨大潜力。在本文中，我们讨论了这样一个管道中 DMF 设备的功能如何高度依赖于与来自机器学习和大数据的不同传感技术和方法的集成。除此之外，我们还研究了 DMF 的能力在某些情况下如何受到已知技术和操作挑战的限制，以及克服这些挑战的综合努力如何成为 DMF 作为计算机辅助领域的主要使能技术发展的关键。生物学框架。