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

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

doi:10.1371/journal.pone.0200962

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

PLoS One. 2018 Jul 25;13(7):e0200962. doi: 10.1371/journal.pone.0200962. eCollection 2018.

Authors

Ping Pan¹, Weifeng Jin², Xiaohong Li², Yi Chen^{1

3}, Jiahui Jiang¹, Haitong Wan⁴, Daojun Yu^{1

5}

Affiliations

¹ Hangzhou First People's Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
² College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
³ Zhejiang Hospital, Hangzhou, Zhejiang, China.
⁴ College of Life Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
⁵ Department of Clinical Laboratory, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Escherichia coli / genetics
Multiplex Polymerase Chain Reaction / methods*
Neural Networks, Computer*
Reproducibility of Results

Grants and funding

This work was supported by Natural Science Foundation of Zhejiang Province (LY17H190001) grant to DJY, http://www.zjnsf.gov.cn/, Health and Family Planning Commission of Zhejiang Province (2015ZDA025) grant to DJY, http://www.zjwjw.gov.cn/, and Health and Family Planning Commission of Hangzhou Manicipality grant (2018ZD001) to DJY, http://wsjsw.hangzhou.gov.cn/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.