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Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable
Author(s) -
Varun B. Kothamachu,
Sabrina Zaini,
Federico Muffatto
Publication year - 2020
Publication title -
slas technology
Language(s) - English
Resource type - Journals
eISSN - 2472-6311
pISSN - 2472-6303
DOI - 10.1177/2472630320931794
Subject(s) - pipeline (software) , microfluidics , computer science , synthetic biology , automation , key (lock) , function (biology) , lab on a chip , digital microfluidics , computer architecture , systems engineering , nanotechnology , engineering , computational biology , materials science , biology , mechanical engineering , evolutionary biology , computer security , electrical engineering , electrowetting , voltage , programming language
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.

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