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 (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 sampl

Engineering the Quantitative PCR Assay for Decreased Cost and Complexity.

The quantitative polymerase chain reaction (qPCR) is an assay of target nucleic acid concentration. Clinical applications of quantitative PCR include measurement of HIV viral load, measurement of bacterial infection, and cancer diagnosis and prognosis. Widespread usage of qPCR, however, is restricted by limited experimental throughput, assay-to-assay variability, and methods of interpreting data that are either cumbersome or lack robustness. This thesis introduces two advances that simplify both the analysis and design of qPCR assays. The first advance, a two parameter mass action kinetic model of PCR (MAK2) was developed for fitting qPCR data in order to quantify target concentration using a single qPCR assay. MAK2-fitting was experimentally validated on three independently generated qPCR datasets and found to quantify data as accurately as the gold-standard method, quantification cycle (Cq) standard curve quantification. The second advance presented, multiplex-MAK2 analysis of monochrome multiplex qPCR (MMQPCR) data, was developed for automated quantification of both targets in duplex qPCR assays without target-specific DNA probes. The MMQPCR assay and multiplex-MAK2-fitting were tested experimentally on a two-dimensional dilution series with known amounts of two synthetic DNA targets. Results indicate that the two-target MMQPCR assay can accurately measure both targets when the target concentration ratio is at least 10:1, and that multiplex-MAK2 quantifies data with similar accuracy to quantification by Cq standard curve. Results obtained from experimental validation using two genetic DNA targets from a microbial coculture further support these conclusions. The results of these experiments suggest that duplex qPCR assays can be performed that are as simple, inexpensive, and accurate as monoplex qPCR assays, yet provide twice as much information. Overall, this work demonstrates the benefits of using biophysics-based qPCR methods. This thesis first provides an overview of the biophysical framework from which current qPCR methods are analyzed. Next, there is an in depth discussion of the analysis methods currently used to analyze qPCR data. The MAK2 model is then derived from first principles and experimentally validated. Multiplex-MAK2-fitting of qPCR data is described and experimentally validated. The thesis concludes with applications of the developed technologies and possible directions for further development of biophysics-based qPCR methods.Ph.D.Chemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/84653/1/gboggy_1.pd

Stepwise kinetic equilibrium models of quantitative polymerase chain reaction

<p>Abstract</p> <p>Background</p> <p>Numerous models for use in interpreting quantitative PCR (qPCR) data are present in recent literature. The most commonly used models assume the amplification in qPCR is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of qPCR data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of qPCR data. New results for kinetic modeling of qPCR are presented.</p> <p>Results</p> <p>Two models are presented in which the efficiency of amplification is based on equilibrium solutions for the annealing phase of the qPCR process. Model 1 assumes annealing of complementary targets strands and annealing of target and primers are both reversible reactions and reach a dynamic equilibrium. Model 2 assumes all annealing reactions are nonreversible and equilibrium is static. Both models include the effect of primer concentration during the annealing phase. Analytic formulae are given for the equilibrium values of all single and double stranded molecules at the end of the annealing step. The equilibrium values are then used in a stepwise method to describe the whole qPCR process. Rate constants of kinetic models are the same for solutions that are identical except for possibly having different initial target concentrations. Analysis of qPCR curves from such solutions are thus analyzed by simultaneous non-linear curve fitting with the same rate constant values applying to all curves and each curve having a unique value for initial target concentration. The models were fit to two data sets for which the true initial target concentrations are known. Both models give better fit to observed qPCR data than other kinetic models present in the literature. They also give better estimates of initial target concentration. Model 1 was found to be slightly more robust than model 2 giving better estimates of initial target concentration when estimation of parameters was done for qPCR curves with very different initial target concentration. Both models may be used to estimate the initial absolute concentration of target sequence when a standard curve is not available.</p> <p>Conclusions</p> <p>It is argued that the kinetic approach to modeling and interpreting quantitative PCR data has the potential to give more precise estimates of the true initial target concentrations than other methods currently used for analysis of qPCR data. The two models presented here give a unified model of the qPCR process in that they explain the shape of the qPCR curve for a wide variety of initial target concentrations.</p