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PubMed · 2059847

ELISA assay optimization using hyperbolic regression.

G M Studnicka
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Abstract

The enzyme-linked immunosorbent assay (ELISA) is routinely used for estimation of specific protein concentration in research and in manufacturing. To optimize both the sensitivity of the assay and its usable dynamic range, it is necessary to adjust the concentrations of reagents, as well as other experimental conditions. Hyperbolic regression is a novel mathematical technique which gives the investigator a quantitative analysis of the assay's kinetic response to variations in reagents and conditions. The equations for hyperbolic regression are easily programmed on a computer spreadsheet or even on a scientific pocket calculator. Hyperbolic regression can be used to facilitate the initial optimization of the assay conditions, or to estimate the concentrations of unknowns on a routine basis directly from the standard curve, or to monitor the long-term degradation in activity of antibody reagents and antigen standards.

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AlgorithmsEnzyme-Linked Immunosorbent AssayMathematical ComputingRegression AnalysisSensitivity and SpecificityJournal Article

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BibTeXRIS

G M Studnicka. 1991. ELISA assay optimization using hyperbolic regression.. https://doi.org/10.1093/bioinformatics%2F7.2.217

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