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4-parameter logistic curve fitting - when and why to use it!
The classic way that unknown concentrations of proteins or biological assays are determined is to first create a standard curve and determine the signal absorbance (Absorbance or fluorescence or luminescence) - in a suitable instrument versus its concentration.
The line of best fit for bioassays such as ELISA and dose response experiments is not always linear because bioassays are only linear in a very specific concentration magnitude. Dose response experiment assessment should use a sigmoidal curve fit for certain interactions. A sigmoidal shape occurs when the x-axis values of rectangular hyperbola is set to a logarithmic scale. Sigmoidal curve fits are analysed with four parameter logistic (4PL) regression models where the Minimum concentration, Maximum concentration, Inflection point (IC50/EC50) and Hill coefficient is analysed. Dose response is often used interchangeably with a related term Concentration Effect Curve. However, the latter is performed outside of living systems such as in cell lines. Excel does not allow such fits as default, so in this video I will show an online tool for 4PL curve fits.
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This video is about 4 parameter logistic curve, 4 parameter model, 4 parameter sigmoidal curve fit, 4pl curve fit, 4 parameter logistic curve formula, ELISA data analysis, non linear regression, linear regression, elisa data, elisa data analysis.
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// ADWOA
I'm Adwoa (Adwoa Biotech), a Biotechnology graduate. I've worked in medical research for years and want to be useful to people new to the lab life. This channel takes you through some of the techniques and concepts I've had to learn working as a Research Assistant to hopefully help others when they are new to the topic/technique.
27 июл 2024
