Programmable design of isothermal nucleic acid diagnostic assays through abstraction-based models

Gaolian Xu, Julien Reboud, Yunfei Guo, Hao Yang, Hongchen Gu, Chunhai Fan (), Xiaohua Qian () and Jonathan M. Cooper ()
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Gaolian Xu: Shanghai Jiao Tong University
Julien Reboud: James Watt School of Engineering, University of Glasgow
Yunfei Guo: Shanghai Jiao Tong University
Hao Yang: Shanghai Jiao Tong University
Hongchen Gu: Shanghai Jiao Tong University
Chunhai Fan: Shanghai Jiao Tong University
Xiaohua Qian: Shanghai Jiao Tong University
Jonathan M. Cooper: James Watt School of Engineering, University of Glasgow

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Accelerating the design of nucleic acid amplification methods remains a critical challenge in the development of molecular tools to identify biomarkers to diagnose both infectious and non-communicable diseases. Many of the principles that underpin these mechanisms are often complex and can require iterative optimisation. Here we focus on creating a generalisable isothermal nucleic acid amplification methodology, describing the systematic implementation of abstraction-based models for the algorithmic design and application of assays. We demonstrate the simplicity, ease and flexibility of our approach using a software tool that provides amplification schemes de novo, based upon a user-input target sequence. The abstraction of reaction network predicts multiple reaction pathways across different strategies, facilitating assay optimisation for specific applications, including the ready design of multiplexed tests for short nucleic acid sequence miRNAs or for difficult pathogenic targets, such as highly mutating viruses.

Date: 2022
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DOI: 10.1038/s41467-022-29101-1

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