Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an...

Area of Science:

• Molecular Biology
• Biotechnology
• Bioinformatics

Background:

• Multiplex quantitative polymerase chain reaction (qPCR) is increasingly utilized across various scientific applications.
• Developing robust mathematical models for multiplex qPCR is crucial for understanding variable interactions and optimizing experimental conditions.

Purpose of the Study:

• To analyze the impact of variable interactions on uni- and multiplex qPCR using Response Surface Method (RSM).
• To optimize qPCR parameters by constructing mathematical models using RSM and a back-propagation neural network-genetic algorithm (BPNN-GA).

Main Methods:

• Response Surface Method (RSM) was employed to analyze variable interactions and optimize parameters for both uni- and multiplex qPCR.
• A back-propagation neural network-genetic algorithm (BPNN-GA) was also used to construct a predictive model.
• Statistical metrics including Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R2) were used for performance evaluation.

Main Results:

• Magnesium ion (Mg2+) concentration was identified as the most significant factor influencing both uni- and multiplex qPCR.
• Both RSM and BPNN-GA successfully generated dynamic models for uni- and multiplex qPCR.
• RSM demonstrated superior predictive performance compared to BPNN-GA, evidenced by better MAE, MSE, and R2 values.

Conclusions:

• RSM is a highly effective method for modeling and optimizing multiplex qPCR parameters.
• Optimal parameters for uni- and multiplex qPCR were successfully determined using the RSM approach.
• The study highlights the importance of Mg2+ concentration for efficient multiplex qPCR assays.