A Mechanistic Model of PCR for Accurate Quantification of Quantitative PCR Data

Background Quantitative PCR (qPCR) is a workhorse laboratory technique for measuring the concentration of a target DNA sequence with high accuracy over a wide dynamic range. The gold standard method for estimating DNA concentrations via qPCR is quantification cycle () standard curve quantification, which requires the time- and labor-intensive construction of a standard curve. In theory, the shape of a qPCR data curve can be used to directly quantify DNA concentration by fitting a model to data; however, current empirical model-based quantification methods are not as reliable as standard curve quantification. Principal Findings We have developed a two-parameter mass action kinetic model of PCR (MAK2) that can be fitted to qPCR data in order to quantify target concentration from a single qPCR assay. To compare the accuracy of MAK2-fitting to other qPCR quantification methods, we have applied quantification methods to qPCR dilution series data generated in three independent laboratories using different target sequences. Quantification accuracy was assessed by analyzing the reliability of concentration predictions for targets at known concentrations. Our results indicate that quantification by MAK2-fitting is as reliable as standard curve quantification for a variety of DNA targets and a wide range of concentrations. Significance We anticipate that MAK2 quantification will have a profound effect on the way qPCR experiments are designed and analyzed. In particular, MAK2 enables accurate quantification of portable qPCR assays with limited sample throughput, where construction of a standard curve is impractical.

[1]  Jana L Gevertz,et al.  Mathematical model of real-time PCR kinetics. , 2005, Biotechnology and bioengineering.

[2]  M. Pfaffl,et al.  A new mathematical model for relative quantification in real-time RT-PCR. , 2001, Nucleic acids research.

[3]  David Bryder,et al.  Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR , 2006, Proceedings of the National Academy of Sciences.

[4]  R. Rutledge,et al.  Sigmoidal curve-fitting redefines quantitative real-time PCR with the prospective of developing automated high-throughput applications. , 2004, Nucleic acids research.

[5]  Weihong Liu,et al.  A new quantitative method of real time reverse transcription polymerase chain reaction assay based on simulation of polymerase chain reaction kinetics. , 2002, Analytical biochemistry.

[6]  M. Pfaffl,et al.  Standardized determination of real-time PCR efficiency from a single reaction set-up. , 2003, Nucleic acids research.

[7]  H. T. Soh,et al.  Integrated genetic analysis microsystems , 2004 .

[8]  Christopher J. Portier,et al.  Absolute estimation of initial concentrations of amplicon in a real-time RT-PCR process , 2007, BMC Bioinformatics.

[9]  Weihong Liu,et al.  Validation of a quantitative method for real time PCR kinetics. , 2002, Biochemical and biophysical research communications.

[10]  Š. Čikoš,et al.  Transformation of real-time PCR fluorescence data to target gene quantity. , 2009, Analytical biochemistry.

[11]  Wei-Shou Hu,et al.  A kinetic model of quantitative real-time polymerase chain reaction. , 2005, Biotechnology and bioengineering.

[12]  Christian Ritz,et al.  Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry , 2008, BMC Bioinformatics.

[13]  Russell Higuchi,et al.  Kinetic PCR Analysis: Real-time Monitoring of DNA Amplification Reactions , 1993, Bio/Technology.

[14]  Christian Ritz,et al.  qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis , 2008, Bioinform..