Regression Tool in MS Excel® Spreadsheets for Biological Data: R-BioXL

A user-friendly MS Excel® spreadsheet as a freeware (R-BioXL) was developed to fit mathematical models to experimental data. (R-BioXL is available to everyone at https://drive.google.com/drive/folders/1GyjT3Z_CJQZu6ASb4LQBlS-ajLa_nF6X?usp=sharing) Initially, users are expected to enter their X-Y data and define their parameters of the model. Then, a model equation should also be entered again by users. Users can visualize data (scatter plot) and model fit (line plot) with the defined initial estimates of parameters on the same graph by default. Squared differences between experimental data and model estimates are calculated automatically. Users can change the initial estimates of the parameters to make the model closer to the data instantly, and Solver Add-In of Excel® should be used to minimize the sum of squared error by changing the parameter values. After the parameters are obtained, standard errors (by using “SolverAid” macro), 95 and 99% confidence intervals of the parameters, p values to determine the statistical significance of the parameters, and goodness-of-fit indices are calculated as the last step. All results can be saved on a different Excel® working page. Whole procedure takes a couple of minutes (~3 to 10 min) depending on the Excel® experience of the user. The utility, accuracy and reliability of the spreadsheet was shown by applying two-parameter (non-linear) Michealis-Menten equation for enzyme kinetics, three-parameter (linear) van Deemter equation for chromatography, and four-parameter (non-linear) modified Gompertz equation for microbial growth. In conclusion, R-BioXL can be safely and freely used to describe the experimental data with Excel® knowledge, without any skills in programming and without additional cost for other software package.