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Related Experiment Video

Updated: Aug 23, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

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Published on: December 15, 2017

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

Kazuhiro Maeda¹, Aoi Hatae², Yukie Sakai²

¹Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. kmaeda@bio.kyutech.ac.jp.

BMC Bioinformatics

|November 2, 2022

Summary

Machine Learning-Aided Global Optimization (MLAGO) improves kinetic modeling by accurately estimating Michaelis constant (K_m) values. This method reduces computational cost and solves parameter non-identifiability issues for better understanding of cellular systems.

Keywords:

Global optimization Kinetic modeling Machine learning Michaelis constant Parameter estimation Simulation Systems biology

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Last Updated: Aug 23, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

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Published on: December 15, 2017

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Published on: April 4, 2014

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

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Published on: January 16, 2016

Area of Science:

Biochemistry
Systems Biology
Computational Biology

Background:

Kinetic modeling is crucial for understanding biochemical systems dynamics.
Parameter estimation, particularly for Michaelis constant (K_m), is essential but faces challenges.
Conventional global optimization is computationally intensive, yields unrealistic values, and suffers from non-identifiability.

Purpose of the Study:

To develop an efficient and accurate method for kinetic parameter estimation.
To address the limitations of conventional global optimization in kinetic modeling.
To improve the estimation of Michaelis constant (K_m) values.

Main Methods:

Proposing the Machine Learning-Aided Global Optimization (MLAGO) method.
Utilizing a machine learning model to predict K_m values based on EC number, KEGG Compound ID, and Organism ID.
Employing predicted K_m values as references for constrained global optimization.

Main Results:

The MLAGO approach successfully estimated K_m values with reduced computational cost.
Achieved good prediction scores (RMSE = 0.795, R² = 0.536) for the K_m predictor.
MLAGO reduced simulation-experimental error and uniquely identified K_m values close to measured ones.

Conclusions:

MLAGO overcomes major parameter estimation problems in kinetic modeling.
Accelerates the process of kinetic modeling for a better understanding of cellular systems.
A web application is available to facilitate MLAGO implementation for researchers.