RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models

Kazuhiro Maeda*, Fred C. Boogerd, Hiroyuki Kurata

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REXstar/JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.

Original languageEnglish
Pages (from-to)30-35
Number of pages6
JournalIPSJ Transactions on Bioinformatics
Volume14
Early online date26 Nov 2021
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
Acknowledgments This work was supported by a Grant-in-Aid for Young Scientists (18K18153), Grant-in-Aid for Transformative Research Areas (B) (20H05743), and Grant-in-Aid for Scientific Research (B) (19H04208) from the Japan Society for the Promotion of Science. This work was further supported by JST PRESTO (JPMJPR20K8). The authors thank Editage for reviewing and editing the manuscript.

Publisher Copyright:
© 2021 Information Processing Society of Japan.

Keywords

  • Evolutionary algorithm
  • Parameter estimation
  • Simulation
  • Systems biology
  • Usability

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