Nonlinear estimation of microbial and enzyme kinetic parameters from progress curve data

Area of Science:

• Biochemistry
• Computational Biology
• Enzymology

Background:

• Estimating kinetic parameters from progress curve data is crucial for understanding biological systems.
• Implicit nonlinear equations present challenges in accurate parameter estimation.
• Existing methods may have limitations in handling complex kinetic models.

Purpose of the Study:

• To develop a robust computer program (BIOKINFIT) for estimating microbial and enzyme kinetic parameters.
• To overcome limitations in parameter estimation for nonlinear equations with implicit dependent variables.
• To provide a reliable tool for analyzing progress curve data in biochemical and microbial studies.

Main Methods:

• Developed BIOKINFIT using nonlinear regression and numerical approximation of substrate concentration.
• Employed the simplex method to minimize the error between observed and predicted substrate depletion data.
• Validated the approach using synthetic data and previously published experimental datasets.

Main Results:

• BIOKINFIT accurately describes experimental substrate depletion data with theoretical curves.
• Kinetic parameter estimates obtained using BIOKINFIT closely match previously reported values.
• The program demonstrates robustness in handling data with varying error magnitudes.

Conclusions:

• BIOKINFIT offers a convenient and robust method for analyzing progress curve data in implicit kinetic expressions.
• The program facilitates accurate estimation of microbial and enzyme kinetic parameters.
• BIOKINFIT is available free of charge, promoting wider accessibility for researchers.