Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach.

Ping Pan,Daojun Yu,Xiaohong Li,Yi Chen,Jiahui Jiang,Haitong Wan,Weifeng Jin

doi:10.1371/journal.pone.0200962

Abstract

Multiplex quantitative polymerase chain reaction (qPCR) has found an increasing range of applications. The construction of a reliable and dynamic mathematical model for multiplex qPCR that analyzes the effects of interactions between variables is therefore especially important. This work aimed to analyze the effects of interactions between variables through response surface method (RSM) for uni- and multiplex qPCR, and further optimize the parameters by constructing two mathematical models via RSM and back-propagation neural network-genetic algorithm (BPNN-GA) respectively. The statistical analysis showed that Mg2+ was the most important factor for both uni- and multiplex qPCR. Dynamic models of uni- and multiplex qPCR could be constructed using both RSM and BPNN-GA methods. But RSM was better than BPNN-GA on prediction performance in terms of the mean absolute error (MAE), the mean square error (MSE) and the Coefficient of Determination (R2). Ultimately, optimal parameters of uni- and multiplex qPCR were determined by RSM.

Highlights

Real-time quantitative PCR can quantitatively analyze a reaction template via the real-time continuous monitoring of the fluorescence signal generated from each cycle of the PCR amplification process
Error of fit (w" Æ s) Error of prediction (w" Æ s) aRSV, human metapneumovirus (HMPV), influenza virus (INF) are three virus used in this study
ARSV, HMPV, INF are three virus used in this study. bMAE means the mean absolute error; MSE means the mean square error

Summary

Introduction

Real-time quantitative PCR (qPCR) can quantitatively analyze a reaction template (nucleic acid) via the real-time continuous monitoring of the fluorescence signal generated from each cycle of the PCR amplification process. This technique has the advantages of being highly specific, highly sensitive, reproducible, accurately quantifiable, and highly automatable[1, 2]. Real-time PCR has been widely applied in fields such as molecular diagnostics, life sciences, agriculture, medicine, and food science[3,4,5] Despite this broad application, there are still difficulties in the practical implementation of the technique, especially in multiplex qPCR systems.

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Discussion

Conclusion