Synthetic data of enzyme reaction rates obtained through Monte Carlo simulations and parameter estimation (Km and Vmax) through different linear and non linear regression methods

The study utilized a modified version of the Henri Michaelis Menten equation to generate synthetic data that incorporates random error, enabling the simulation of various experimental conditions, including the presence of outliers. Key parameters, such as maximum velocity (Vmax) and Michaelis constant (Km), were explicitly defined, and random error was modeled using a normal distribution. Different scenarios were created by varying data size, outlier contamination, outlier deviation, and substrate distribution (linear or logarithmic), with 1500 replicates generated for each scenario using MATLAB. Thereafter, multiple methods were employed for estimating kinetic parameters, including Lineweaver-Burk (LB), Weighted Lineweaver-Burk (WLB), Eadie-Hofstee (EH), Hanes-Woolf (HW), nonlinear regression (NLR), and robust nonlinear regression (RNLR). The resulting estimates (Km and Vmax) and statistical information (Standard error and 95% confidence intervals) were obtained and stored.

This dataset contains:
- Code for Monte Carlo simulations employed to obtain synthetic data of enzyme reaction rates
- Code to perform different regression methods with synthetic data obtained through Monte Carlo simulations
- Data of scenarios' conditions tested in Monte Carlo simulations: Data size (n), Standard deviation (σ), Outlier contamination (O), Outlier deviation (O_σ), and Substrate distribution.
- Synthetic data obtained via Monte Carlo simulations
- Parameter estimates (Km, Vmax) and statistical data (Standard Errors and 95% Confidence Intervals) obtained through different regression methods: Lineweaver-Burk, Weighted Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf, Nonlinear Regression, and Robust Nonlinear Regression.