Fit 4PL/5PL logistic models to your standard curve data. Interpolate unknowns with QC flags, residual plots, and publication-ready exports — data never leaves your browser.
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When to use
Do not use for
Fewer than 6 points risks underdetermining the 4PL model. The curve fit needs adequate data at both plateaus and the transition region to reliably estimate all four parameters.
A high with systematic residual patterns (U-shape, trend) indicates model mis-specification. The residual plot is more informative than alone.
ELISA data almost always has variance that increases with signal. Without 1/Y or 1/ weighting, the fit is biased toward high-concentration standards, sacrificing accuracy at the sensitive low end.
The extra parameter in 5PL can overfit noisy data. Use "Auto" mode — it runs an F-test and only recommends 5PL when the asymmetry parameter significantly improves the fit.
A single mis-pipetted standard can distort the entire curve. Exclude the level, re-fit, and document the exclusion in your audit trail.
Regulatory guidelines (FDA bioanalytical guidance) require reporting the quantifiable range. This tool computes LLOQ/ULOQ from back-calculated recovery automatically.
Levenberg-Marquardt nonlinear least squares optimization with 1/Y and 1/ weighting options. Implements the Gottschalk & Dunn (2005) five-parameter logistic model.
Last validated 2026-03-20. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.
ConductScience ELISA Curve Fitter (v1.0). ConductScience, Inc. 2026. Available at: https://conductscience.com/tools/elisa-curve-fitter
The 4PL (four-parameter logistic) model describes a symmetric sigmoidal curve: y = D + (A−D) / [1 + (x/C)^B]. The four parameters are the minimum asymptote (A), Hill slope (B), inflection point or EC50 (C), and maximum asymptote (D). This is sufficient for most well-behaved sandwich ELISAs.
The 5PL model adds an asymmetry parameter (E): y = D + (A−D) / [1 + (x/C)^B]^E. When E ≠ 1, the curve approaches its upper and lower plateaus at different rates — a pattern common in competitive ELISAs and multiplex assays. In practice, 5PL fitting can substantially reduce residual error for asymmetric dose-response curves, particularly at the extremes of the standard range.
In an unweighted fit, all data points contribute equally to the sum of squared residuals. But ELISA data is heteroscedastic — the variance of OD readings increases with signal magnitude. Without weighting, high-concentration standards dominate the fit, sacrificing accuracy at the low end where sensitivity matters most.
1/Y weighting (inverse response weighting) gives each point influence proportional to 1/OD, restoring balance. 1/ provides even stronger correction, which is preferred when low-concentration precision is critical (e.g., biomarker detection near the limit of quantification).
A good standard curve is the foundation of accurate ELISA quantification. Key QC metrics include:
Back-calculated recovery: For each standard, the fitted curve is used to back-calculate its concentration from the measured OD. Recovery should be 80-120%. Points outside this range indicate a poor fit or a bad standard preparation.
%CV of replicates: The coefficient of variation between replicate ODs at each standard level should be < 20%. High CV suggests pipetting error, incomplete mixing, or plate inconsistency.
