Model-Based qPCR Cycle Threshold Determination
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Solution Overview
Problem
Current methods for determining the cycle threshold in PCR amplification curves are complex and prone to errors due to the need for manual threshold setting and require numerous parameters to model the amplification curve accurately, which can be noisy and insensitive to parameter changes.
Innovation Solution
A computer-implemented method using a model-based approach to determine the cycle threshold by creating a modeled efficiency curve and amplification curve through constrained nonlinear optimization, reducing the number of parameters needed and improving noise handling, allowing for more stable and accurate cycle threshold determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed model with many parameters is used to model the PCR amplification curve, then the model accuracy may improve, but the model complexity increases and becomes difficult to use successfully
Solution Approach 1:
The patent extracts and eliminates unnecessary parameters from complex PCR amplification models, retaining only the essential parameters that significantly impact cycle threshold determination. This simplification removes redundant complexity while preserving measurement accuracy by focusing on the most influential model components.
Solution Approach 2:
The patent transforms the modeling approach by changing from a high-parameter model to a reduced-parameter model. This parameter reduction strategy maintains adequate modeling accuracy for cycle threshold determination while dramatically simplifying the model structure and improving computational efficiency and usability.
2Ease of operation
If manual threshold setting is used, then the method is simple to implement, but the results are prone to errors and require user subjectivity
Solution Approach 1:
The patent implements an automated cycle threshold determination system that performs modeling and threshold calculation without requiring manual user intervention. The simplified model automatically processes the amplification curve data and outputs the cycle threshold value, eliminating subjective user bias while maintaining ease of operation through automated computation.
Solution Approach 2:
The patent replaces the manual mechanical process of visual threshold setting with an automated computational modeling approach. The simplified mathematical model automatically determines the cycle threshold based on amplification curve characteristics, substituting human judgment with algorithmic processing that is both reliable and easy to implement.
3Quantity of substance
If the beginning and later points in an amplification curve are used for modeling, then more data points are available, but these points are noisy and insensitive to parameter change
Solution Approach 1:
The patent applies local quality by selectively using only the middle portion of the amplification curve for modeling, where the signal is most sensitive to parameter changes. This regional selection excludes the noisy beginning and later points, focusing the analysis on the high-quality data segment that provides maximum information for accurate cycle threshold determination.
Solution Approach 2:
The patent uses only a partial segment of the available amplification curve data rather than all data points. By selectively applying the model to the middle portion of the curve where parameter sensitivity is highest, the method achieves better precision by using fewer but higher-quality data points instead of all available points.
Data Source
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AI summary
A method for determining a cycle threshold for a PCR amplification curve is provided. The method includes receiving a data set for a plurality of biological samples for a PCR amplification reaction. The data set includes a plurality of amplification curves, each amplification curve associated with a biological sample of the plurality of biological samples. The method further includes performing a nonlinear optimization comprising a fit of each amplification curve to a complementary modeled amplification curve to determine a best-fit set of parameters for a modeled efficiency curve and associated amplification curve. The modeled amplification curve is based on a modeled efficiency curve. The method includes determining a cycle threshold value for each biological sample based on a complementary relationship of the modeled efficiency curve to the modeled amplification curve. In an embodiment, the nonlinear optimization is a constrained nonlinear optimization.