a mechanistic model of pcr for accurate quantification of quantitative pcr datapcr的机械模型准确定量pcr的量化数据.pdfVIP
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a mechanistic model of pcr for accurate quantification of quantitative pcr datapcr的机械模型准确定量pcr的量化数据
A Mechanistic Model of PCR for Accurate Quantification
of Quantitative PCR Data
Gregory J. Boggy*, Peter J. Woolf
Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
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 (Cq) standard curve quantification, which requires the time- and labor-intensive construction of
a Cq 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 Cq 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 Cq 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 stan
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