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Updated: Jun 3, 2026

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway.

Ahmad Muhaimin Ismail¹, Muhammad Akmal Remli^2,3, Yee Wen Choon^2,3

¹Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

Journal of Integrative Bioinformatics

|June 21, 2023

Summary

This summary is machine-generated.

Accurate kinetic parameters for systems biology models are crucial. The Artificial Bee Colony (ABC) algorithm effectively estimates these parameters for the Saccharomyces cerevisiae fermentation pathway, improving simulation accuracy.

Keywords:

artificial bee colony algorithm artificial intelligence bioinformatics data science fermentation pathway parameter estimation

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Area of Science:

Systems Biology
Metabolic Engineering
Computational Biology

Background:

Accurate kinetic parameters are essential for simulating in vivo biological processes in systems biology.
Parameter estimation for complex biological models, like the Saccharomyces cerevisiae fermentation pathway, is challenging due to model nonlinearity and the inability to directly measure kinetic parameters.

Purpose of the Study:

To propose and evaluate the Artificial Bee Colony (ABC) algorithm for estimating kinetic parameters in the Saccharomyces cerevisiae fermentation pathway.
To obtain more accurate kinetic parameter values for improved simulation of in vivo processes.

Main Methods:

Utilized the Artificial Bee Colony (ABC) algorithm, a nature-inspired optimization technique.
Applied the ABC algorithm to estimate six parameters for a key metabolite in the Saccharomyces cerevisiae fermentation pathway.
Compared the performance of the ABC algorithm against other estimation algorithms.

Main Results:

The ABC algorithm demonstrated superior performance compared to other estimation algorithms.
The kinetic parameter values estimated by ABC were more accurate for the simulated model.
The majority of estimated kinetic parameters closely matched the experimental data.

Conclusions:

The Artificial Bee Colony (ABC) algorithm is an effective tool for parameter estimation in complex biological models.
Accurate kinetic parameter estimation using ABC enhances the reliability of systems biology simulations for the Saccharomyces cerevisiae fermentation pathway.
This approach can lead to more precise understanding and optimization of metabolic processes.