renz

renz analyzes enzyme kinetic data that deviate from Michaelis-Menten kinetics and estimates kinetic parameters using multiple fitting and regression approaches.

Key Features:

• Methodological diversity: renz provides multiple analysis methods categorized by data-fitting approach and data-manipulation requirement.
• Data fitting approaches: Methods fit either single progress curves (substrate concentration over time) or analyze the dependency of initial rates on substrate concentration via differential rate equations.
• Data manipulation options: Methods include approaches that transform experimental data to obtain linear functions and approaches that perform direct non-linear regression without such transformations.
• Error minimization: The package offers analytical options that reduce biases from unweighted regression analyses and incorrect data transformations during kinetic parameter estimation.

Scientific Applications:

• Enzyme kinetic parameter estimation: Estimation of kinetic parameters from progress-curve and initial-rate datasets, including cases that do not follow Michaelis-Menten kinetics.
• Biochemistry and pharmacology research: Analysis of enzyme behavior to support experimental design, hypothesis testing, and data interpretation in biochemistry, molecular biology, and pharmacology.

Methodology:

Computational methods explicitly include fitting single progress curves (substrate concentration vs time), analyzing initial-rate dependencies using differential rate equations, applying data linearization transformations, and performing direct non-linear regression.