Sigmoidal or logistic curve fit

A number of publications have suggested an efficiency calculation on the basis of all fluorescence data points (starting at cycle 1 up to the last cycle), according to a sigmoidal or logistic curve fit model (Tichopad et al., 2003; Tichopad et al, 2004; Liu and Saint, 2002a; Liu and Saint, 2002b; Rutledge, 2004). The advantage of such models is that all data points will be included in the calculation process and no background subtraction is necessary. The efficiency will be calculated at the point of inflexion (cycle 27.06 shown in Figure 3.5) at absolute maximum fluorescence increase.

In the four-parametric sigmoid model (eq. 10), x is the cycle number, f(x) is the computed function of the fluorescence in cycle number x, y0 is the background fluorescence, a is the difference between maximal fluorescence reached at plateau phase and background fluorescence (i.e. the plateau height), e is the natural logarithm base, x0 is the co-ordinate of the first derivative maximum of the model or inflexion point of the curve, and b describes the slope at x0 in the log-linear phase (Tichopad et al., 2004). But a a


Figure 3.5


Figure 3.5

Efficiency calculation on the basis of a four-parametric sigmoid model (eq. 10).

the derived slope parameters generated by the sigmoidal or logistic models, e.g. b, can not directly compared with the 'real PCR efficiency.' The advantages of the four-parametric sigmoid model is that it is easy to perform, is a good estimator for the maximum curve slope with high correlation between replicates (r > 0.99) and the algorithm can easily implemented in analysis software. The resulting efficiencies are comparable to the latter method and range from 1.35 to 1.65.

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