MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling.

ABSTRACT:

Background

Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (K_m), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem).

Results

To solve these pro

SUBMITTER: Maeda K 

PROVIDER: S-EPMC9624028 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature