Estimation of Kinetic Parameters of PCR Process Using Genetic Algorithm

Polymerase chain reaction (PCR) is a technology which amplifies the special DNA segment in vivo. Since it is invented in 1985, this elegant technology has been widely used in much field, especially in neuroscience, such as the cloning of neuropeptide or protein gene and so on. Although the procedure of PCR is simple, due to numerous and diversity application of PCR which are being developed across all disciplines of diagnostic pathology and research, there is no uniform reaction condition for all kinds of DNA segment with different length and complexity. So the optimal experimental conditions are necessary to be determined. The mathematic model with predictive capability is significant utility for the optimization of the reaction conditions. A Kinetic model of PCR is developed to simulate the PCR process. For the kinetic parameters which have not been measured in experiment, a genetic algorithm was applied to get the best fit between the calculation results and the experimental data. The results show that the predictive model of PCR with the parameters optimized is in good agreement with the experiment results. Moreover, the parameters optimization is not affected by the initial conditions, the kind of template and the different laboratory.