Simulation of between repeat variability in real time PCR reactions

• Published in: PloS one Vol. 7; no. 11; p. e47112
• Main Authors: Lievens, Antoon, Van Aelst, Stefan, Van den Bulcke, Marc, Goetghebeur, Els
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26-11-2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Analysis of Variance; Applied mathematics; Bioinformatics; Biology; Computer science; Computer Simulation; Consumption; Decision analysis; Deoxyribonucleic acid; DNA; Efficiency; Enzymes; Error analysis; Fluorescence; Mathematical models; Mathematics; Methods; Models, Statistical; Molecular biology; Parameter estimation; Polymerase chain reaction; Process parameters; Public health; Real time; Real-Time Polymerase Chain Reaction - standards; Reproducibility; Reproducibility of Results; Sensitivity and Specificity; Side reactions; Simulation; Soybeans; Statistical analysis; Statistical models; Stochasticity; Variation; Belgium
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0047112

While many decisions rely on real time quantitative PCR (qPCR) analysis few attempts have hitherto been made to quantify bounds of precision accounting for the various sources of variation involved in the measurement process. Besides influences of more obvious factors such as camera noise and pipetting variation, changing efficiencies within and between reactions affect PCR results to a degree which is not fully recognized. Here, we develop a statistical framework that models measurement error and other sources of variation as they contribute to fluorescence observations during the amplification process and to derived parameter estimates. Evaluation of reproducibility is then based on simulations capable of generating realistic variation patterns. To this end, we start from a relatively simple statistical model for the evolution of efficiency in a single PCR reaction and introduce additional error components, one at a time, to arrive at stochastic data generation capable of simulating the variation patterns witnessed in repeated reactions (technical repeats). Most of the variation in C(q) values was adequately captured by the statistical model in terms of foreseen components. To recreate the dispersion of the repeats' plateau levels while keeping the other aspects of the PCR curves within realistic bounds, additional sources of reagent consumption (side reactions) enter into the model. Once an adequate data generating model is available, simulations can serve to evaluate various aspects of PCR under the assumptions of the model and beyond.