RT Journal Article
T1 renz: An R package for the analysis of enzyme kinetic data.
A1 Aledo-Ramos, Juan Carlos
K1 Bioinformática
K1 Enzimas - Cinética
AB Background: Complex enzymatic models are required for analyzing kinetic dataderived under conditions that may not satisfy the assumptions associated withMichaelis–Menten kinetics.Results: To fill the existing gap between highly specialized and general-purposesoftware, we have developed an easy-to-use R package, renz, designed for accurateand efficient estimation of enzyme kinetic parameters. The package provides differentmethods that can be clustered into four categories, depending on whether they arebased on data fitting to a single progress curve (evolution of substrate concentrationover time) or, alternatively, based on the dependency of initial rates on substrate concentration(differential rate equation). A second criterion to be considered is whetherthe experimental data need to be manipulated to obtain linear functions or, alternatively,data are directly fitted using non-linear regression analysis. The current programis a cross-platform, free and open-source software that can be obtained from the CRANrepository. The package is accompanied by five vignettes, which are intended to guideusers to choose the appropriate method in each case, as well as providing the basictheoretical foundations of each method. These vignettes use real experimental data toillustrate the use of the package utilities.Conclusions: renz is a rigorous and yet easy-to-use software devoted to the analysisof kinetic data. This application has been designed to meet the needs of users who arenot practicing enzymologists, but who need to accurately estimate the kinetic parametersof enzymes. The current software saves time and minimizes the risk of makingmistakes or introducing biases due to uncorrected error propagation effects.
PB BMC
YR 2022
FD 2022-05-16
LK https://hdl.handle.net/10630/40144
UL https://hdl.handle.net/10630/40144
LA eng
NO Aledo, J.C. renz: An R package for the analysis of enzyme kinetic data. BMC Bioinformatics 23, 182 (2022). https://doi.org/10.1186/s12859-022-04729-4
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026