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

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  • Gregory J Boggy
  • Peter J Woolf

Abstract

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.

Suggested Citation

  • Gregory J Boggy & Peter J Woolf, 2010. "A Mechanistic Model of PCR for Accurate Quantification of Quantitative PCR Data," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-7, August.
  • Handle: RePEc:plo:pone00:0012355
    DOI: 10.1371/journal.pone.0012355
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    Cited by:

    1. Andrew McDavid & Lucas Dennis & Patrick Danaher & Greg Finak & Michael Krouse & Alice Wang & Philippa Webster & Joseph Beechem & Raphael Gottardo, 2014. "Modeling Bi-modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-10, July.

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