Research On The Software System For Q-PCR Applications Based On Improved Quantitative Mathematical Model

Polymerase chain Rection(PCR) technology, as one of the best tools to research biomedical science, has already been widely used in bioscience, drug discovery, disease diagnosis, and other fields after 30 years of development. The market of real-time Quantitative PCR instruments is basically monopolized by foreign companies so far while the research of PCR technology in our country is started late. In this circumstance, this paper starts from the quantitative mathematical model of PCR and studies current research of it at home and abroad, finding out that the mathematical model based on Logistic function and proposed by Australian scholars, Liu and Saint, has high quantitative precision. After making appropriate improvements and optimization of the model, its validity and applicability is verified by Matlab simulation in this paper. Then the quantitative PCR software based on the model is designed and developed as upper computer software of Q-PCR instrument developed by our research group.The quantitative PCR software is modularized after software requirement analysis. It is divided into two main parts, experimental design and data analysis. According to the logical order when users set up an experiment, several intuitive and friendly interfaces is designed and programmed to achieve the various functions of the software respectively. In addition, the quantitative PCR software also needs to communicate with the lower computer which is controlled by an ARM processor. So the RS232 serial port is used for communication and the whole software system of Q-PCR instrument,thus, is integrated including temperature control module, motion control module and data acquisition module.In the end of this paper, a simple absolute quantitative PCR experiment is done to test the functionality of quantitative PCR software and the Q-PCR software system. The data obtained from the experiment is fit by different quantitative mathematical models and the compare results indicate that the improved quantitative mathematical model in this paper has better reproducibility and precision than the exponential growth model.